visummodelwiki - User contributions [en]

6.1 General Overview

2021-02-02T20:23:21Z

Bstabler: Updated to 2045 CFP

<p><strong><em>Network Change</em></strong></p><p class="BodyParagraph">There are four networks officially part of the LRTP
“2045 Path Forward” effort. These are for the years 2015, 2018 (E+C), 2030 and
2045. The information regarding the period of
construction related to the planned roadway projects within the North Florida
TPO’s area is listed in [http://pathforward2045.com/wp-content/uploads/2020/05/2045-Cost-Feasible-Plan-Tech-Report-VR03-5_6_20.pdf 2045 Cost Feasible Plan Technical Report].</p><p></p><h4 id="id-6.1GENERALOVERVIEW-EmploymentChange"><em>Employment Change</em></h4><p class="BodyParagraph">In future, some areas may see growth/decline in employment due to increase/decrease in business establishments or some other reasons. In addition, the distribution of different employment types could change over the years.</p><p class="BodyParagraph">In order to reflect those changes in the model, the DaySim buffered parcel file needs to be updated - \Master\{SCENARIO_DIR}\
Input\DaySimInput\02_Parcel\buffered_maz_year.dat.</p><p class="BodyParagraph">To update the file, two steps should be performed:</p><ul><li>If employment at a TAZ is same, including in different sectors, but distribution of employment in microzones within the TAZ have changed then either of the two should be undertaken:
<ul><li>Edit the land-use file manually.</li><li>Get new block level controls (with employment in 2-digit NAICS categories)
and run the allocation tool (see section 5.1 |) to obtain an updated buffered parcel file.</li></ul></li><li>If employment (or/and distribution of employment sectors) at a TAZ is different, the new TAZ file should be used to run the allocation tool (see section 5.1 |). The tool will produce an updated parcel file.</li><li>The first step would create a new land-use file and an updated TAZ file, if necessary. The new land-use file should be provided as input to the buffering tool (see section 5.1 | )and generate a new buffered parcel file. This file should then be replaced in the DaySimInput folder.</li></ul><h4 id="id-6.1GENERALOVERVIEW-PopulationChange"><em>Population Change</em></h4><p class="BodyParagraph">After a base year population has been synthesized, new synthetic populations may need to be generated based on the type scenario being run or the policy being tested. For example, a change in the network may not require the population to be synthesized again. On the other hand, if a land-use policy variable is being tested which would result in significantly different patterns in the population and employment levels in the TAZs than in the base case, a new synthetic population would be have to be generated. The same would be the case for a future year run when the population mix or distribution of specific population characteristics may deviate from those in the base synthetic population. Population is likely to increase in future, however, it may not increase proportionately geographically. A change in employment may also alter the existing geographic distribution of the population. To accommodate a change in population following three input files need to be updated:</p><ul><li>Buffered parcel file (\Master\{SCENARIO_DIR}\Input\DaySimInput\02_Parcel\
buffered_maz_year.dat)</li><li>Household file
(\Master\{SCENARIO_DIR}\Input\DaySimInput\03_Household\household_year.dat)</li><li>Person file
(\Master\{SCENARIO_DIR}\Input\DaySimInput\04_Person\person_year.dat)</li></ul><p class="BodyParagraph">To update the buffered parcel file, the similar steps
as employment change accommodation needs to be performed. First, the land-use file (output of the allocation tool) is updated and then it is used in the buffering tool to generate a new buffered parcel file that goes into DaySim.</p><p class="BodyParagraph">Synthetic population data (household and person files) would also need updates. As previously described, the synthetic population is
generated using PopulationSim. The marginal control files for population synthesis need to be updated to reflect the new control totals. After that synthetic population needs to be generated using the new input files (Please refer to PopulatinSim guide for synthesis instructions).</p>

6.3 Creating a New Scenario

2021-02-02T19:52:05Z

Bstabler: Changed Base2010 to Base2015

[[Category:6.0 Configuring a Scenario]]

<p>The NERPM-AB catalog has a Master scenario, and scenarios are hierarchical
in nature. From the Master, you can create scenarios that will appear as
children of the Master. A child may be considered as a variation on its parent.
You can also create children of the preexisting scenarios, such as
Base2015.</p>

<p>A child scenario will inherit key values from its parent. For example, a
catalog may have some keys, one of which is named “Year.” This may be set to 10
in the Base2015 scenario. If a new scenario “New Road” is created as a child of
the Base2015, then the new scenario will also initially have a value of 10 for
the Year key. You may then edit the “New Road” scenario, updating the value of
Year to 15. You may choose to update some of the other keys of the scenario, or
you can leave them the same as in the Base2015. Running the “New Road” scenario
will substitute the value of 15 into the application for the Year key, giving
new results and providing a comparison with the base situation.</p>

<p>• To add a child scenario, click Add Child on the Scenario ribbon tab. Or,
right-click under Scenario in Scenario Manager and choose Add Child.</p>

<p>An Edit Box opens where you type the name of the new child scenario. To
cancel the operation, press ESC. To complete and save the name entered, press
ENTER.</p>

<p>After the name is entered, the Scenario Properties dialog is displayed to
allow entry of the scenario code, and then the Edit Scenario dialog will also
be displayed. This allows entry of the key values for the new scenario. An
additional modification has been made to NERPM-AB so that the first step of the
model process is to copy all of the input files from the parent scenario in the
child’s input directory.</p>

<p>If modifications need to be made to the scenario inputs before running the
new scenario, then the Prepare Data Folders step can be run manually to copy
the inputs. First, the NERPMAB1 App flow chart should be visible with the new
scenario active as shown in Figure 6-1. If the new scenario is not active, it
can be activated by selecting a different scenario and selecting the new
scenario again. Then the "Prepare Data Folders" <span style=
"color: rgb(38,38,38);">step can be run by itself by double-clicking on that
PILOT step pointed to in the following image. The input files will be copied
and can then be edited using the standard Cube tools.</span></p>

<p><strong><span style="color: rgb(38,38,38);">Figure 6-1 App Flow
Chart</span></strong>
</p>

[[File:Figure 6-1.png]]

<p>
</p>

<p>One important consideration when creating new scenarios is that Cube always
copies the key values from the parent of the new scenario. This is the case
even if the scenario is created as a "sibling" of an existing
scenario. For example, if the new scenario is created as a sibling of CF2040,
Cube will actually create the scenario as a new child of the parent scenario of
CF2040, inheriting the parent’s settings (Master in this case). Any new child
of Master will be identical to Base2015, and if another year is desired then
the appropriate network, land use, and key value inputs with need to be added
to the scenario manually. The inheritance process will be applied to any
scenario created as a child of one of the existing scenarios in that all inputs
and key values will be inherited from the parent. Any scenario specific changes
for the new scenario need to be applied after creating the scenario and running
the Prepare Data Folders step.</p>

<p><em><strong>Create a Master Network Alternative</strong></em>
</p>

<p>If roadway network changes are needed for the new scenario, or a new year is
being used, then a new alternative should be created in the Master network.
This is needed because NERPM-AB uses a single master net file for all
scenarios. The network alternative is specified by the scenario key values for
Alt and Year. The <strong>Create an Alternative</strong> app in NERPM-AB can be
used to create the needed alternative specific fields in the master network.
Care must be taken in modifying the network to avoid making unintentional
changes to other alternatives. Additionally, several files in the scenario
input directory include the Year and Alt in their file name, so the names must
correspond with the scenario Alt and Year keys.</p>

<p>Follow these steps (as illustrated in Figure 6-2)</p>

<ol>
<li>Select the <strong>Create an Alternative</strong> app in the App section of
the scenario manager</li>

<li>Double-click on the scenario for which you want to create a new network
alternative</li>

<li>Specify the key values for the new and existing alternative letters and
years</li>
</ol>

<ul>
<li>WARNING: If the new alternative letter and year correspond to an existing
alternative in the master network, then they will be replaced running this
app.</li>
</ul>

<ol>
<li>Click on the Save button and then the Run button to create the
alternative</li>

<li>Click on the Close button to exit Create an Alternative</li>

<li>Select the <strong>NERPMAB1</strong> app in the App section to return to
the main model</li>
</ol>

<p><strong>Figure 6-2 Steps in Creating a New Scenario</strong>
</p>

[[File:Figure 6-2.png]]

7.3 Version NERPMAB2

2021-02-02T19:49:11Z

Bstabler: Updated content for special generators

[[Category:7.0 Version Updates]]
'''7.3 VERSION NERPM-AB2'''

This section documents the set of improvements that were made to the NERPM model for the release of NERPM-AB2. The improvements focused on:

*Base year updated from 2010 to 2015
*Re-built all land use inputs for 2015 and future
*Switched from parcels to microzones to ease data preparation
*Switched from PopGen to PopulationSim
*Updated the highway network to 2015 (NFTPO)
*Updated the transit network to 2016 (JTA)
*Updated external traffic volumes and distributions based on Bluetooth OD data (FDOT)
*Upgraded from the NFTPO specific version of DaySim to the multiple-agency (a.k.a. core) version, which is faster, more stable, and includes new features such as Transportation Network Companies (TNC) mode (i.e. Uber and Lyft)
*Updated future year scenarios

The details of these improvements can also found in [[Media:NERPM_AB_FinalReport_v7.docx|NERPM_AB_FinalReport_v7.docx (DEC 2020)]].

<h2> Parcel to Microzone (MAZ) </h2>

In the previous version of the model, the unit of spatial analysis was parcels. There were approximately 700,000 parcels in the region, which made it difficult to maintain, edit and attribute, both the existing and alternative scenarios. This led to the creation and use of microzones as an alternative, which is halfway between parcels and TAZs and are easier to maintain. As you can see in the table, there are around 55 thousand microzones against 700 thousand parcels. This makes it much easier for the user to update land use information such as employment, enrollment etc. The total number of TAZs in this version is 1862. The plot shows the TAZ and the MAZ layer around downtown Jacksonville. This shows how TAZs and MAZs are related. You can see in the plot that MAZs in downtown tend to be smaller than the ones in the periphery. If you have any questions about the development of MAZs, please refer to the model documentation for more details.
{| class="wikitable"
|-
! <br />County
! <br />Number of Parcels
! <br />% Freq
! <br />Number of MAZ
! <br />% Freq
! <br />Number of TAZ
! <br />% Freq
|-
| <br />Baker
| <br />12,490
| <br />1.78
| <br />1,735
| <br />3.14
| <br />29
| <br />1.56
|-
| <br />Clay
| <br />84,529
| <br />12.02
| <br />7,796
| <br />14.13
| <br />184
| <br />9.88
|-
| <br />Duval
| <br />355,805
| <br />50.59
| <br />28,263
| <br />51.22
| <br />1,281
| <br />68.80
|-
| <br />Nassau
| <br />47,443
| <br />6.75
| <br />4,455
| <br />8.07
| <br />108
| <br />5.80
|-
| <br />Putnam
| <br />102,053
| <br />14.51
| <br />6,652
| <br />12.05
| <br />44
| <br />2.36
|-
| <br />St. Johns
| <br />100,950
| <br />14.35
| <br />6,283
| <br />11.39
| <br />216
| <br />11.60
|-
| <br />Total
| <br />703,270
| <br />100.00
| <br />55,184
| <br />100.00
| <br />1,862
| <br />100.00
|}

<h4> Figure: MAZ and TAZ layer in Downtown Jacksonville </h4>

[[File:Maztaz.jpg]]

<h2> Land-use data updated to base year 2015 </h2>

Once the microzones were formed the following land-use data were updated for the base year 2015:

<h3> Household Data </h3>

The household data at parcel level was aggregated to microzone level for the base year 2015.

<h3> Employment Data </h3>

The employment data was updated using three sources of data:

*The 2010 parcel employment data projected to 2015

*Data provided by Infogroup and edited by North Florida TPO

*Data provided by Cambridge Systematics as described in their June 30, 2017 memorandum “Development of 2015 Employment Data” for FDOT

Please refer to the model documentation for the details on how these data sources were utilized.

<h3> School Enrollment Data </h3>

School enrollment data from the Florida Department of Education (FDOE) provided by North Florida TPO was used to develop school enrollment data at microzone level for the base year 2015. The details of this development process can be found in the model documentation.

<h3> Parking </h3>

The parking data was derived from Florida DOT parking shapefiles "Parking surface" and "g100freeway". The website [https://www.bestparking.com/jacksonville-fl-parking/ Jacksonville, FL Parking] was also used along with the shapefiles to develop parking data.

<h3> Lodging </h3>

A list of 367 known hotels, motels, and bed and breakfasts from the Florida Department of Business and Professional Regulation provided by North Florida TPO was used to develop lodging information.

<h3> Special Generators </h3>

Jacksonville International Airport (JAX) and St. Augustine are the two special generators in the model.

The Airport had an attraction value of 15,000 in 2010. Based on several sources, RSG determined that the passenger traffic at the airport has decreased slightly between 2010 and 2015. CAPA – Centre for Aviation noted an overall downward trend in traffic since 2008 but traffic began to pick up in 2013 . The airport’s website lists the 2010 and 2015 number of passengers as 5,602,000 and 5,502,000 respectively, which represents a 1.8% reduction in traffic. FDOT’s 2015 Airport Profile of JAX showed similar findings. Given the small change over the years, we recommend leaving the airport’s attraction value at 15,000 for 2015.

St. Augustine had an attraction value of 2,288 in 2010. This number accounts for tourists coming to the area. In 2010 and 2015, St. Johns County had 3.4 million population and 6.3 million visitors, respectively, which represents an 85% growth over the five-year period. The 2015 number was thought to be an anomaly because in 2015 the City’s celebrate its 450th birthday. However, in 2016 the City had 6.8 million visitors, so an 85% increase may be reasonable. Increasing the attractions value by 85% to 4,240 could have a noticeable impact on links in the vicinity of this TAZ.

<h2> PopulationSim </h2>

*PopulationSim is an open platform for population synthesis

*Replaced PopGen in this version of NERPM-AB

*Has better demographic and geographic methods compared to PopGen

*Actively maintained by the travel modeling community

*Run only when there are major changes in the landuse data

[http://https://activitysim.github.io/populationsim/# PopulationSim Wiki]

<h3> Input Data </h3>

The main inputs to a population synthesizer are

*Disaggregate population samples (Seed Sample)

*Marginal control distributions (Control variables)

The Seed sample is obtained from the [https://www.census.gov/programs-surveys/acs/data/pums.html Census Public Use Microdata Sample (PUMS)] and the Marginal distributions of person and household attributes are obtained from [https://www.bebr.ufl.edu/ Bureau of Economic and Business Research (BEBR)] and Census respectively.

The next step is the preparation of inputs to PopulationSim which includes:

<h4> Geographic cross-walk </h4>

PopulationSim can handle any number of nested geographies. The geography level (hierarchy) selected for NERPM-AB 2015 to which the marginal distributions are specified are as follows:

*Meta Geography: Super County (SCOUNTY)

*Seed Geography: PUMA

*Sub-seed Geography: TAZ and MAZ

Super-County is chosen instead of County due to overlapping of county and PUMA boundaries. Super County is the combination of multiple County to form one geographic area.

<h4> Seed population (Household and Person tables) </h4>

One of the main requirements for the seed sample is that it should be representative of the modeling region. The seed sample is obtained from PUMS dataset. The PUMS data contain five years of household records. The seed sample should be representative of the modeling region. It must contain all the specified control variables, as well as any variables that are needed for the travel model but not specified as controls. The PUMS data is downloaded from PUMS website and it is extracted both demographically and geographically for the North Florida TPO region using PUMA codes conforming to the region. There are 11 PUMA regions in the North Florida TPO area. The seed sample must include an initial weight field. The model setup includes the Seed sample that contains a weight field, WGTP, which is used for control of total households.

<h4> Controls </h4>

Controls or targets are the marginal distributions that form the constraints for the population synthesis procedure. The objective of the population synthesis procedure is to produce a synthetic population with attributes matching these marginal distributions. The controls were developed at three geographic levels:

<h5> Super County </h5>

The raw input data was downloaded at county level from [https://www.bebr.ufl.edu/sites/default/files/Research%20Reports/projections_2016_asrh.xlsx BEBR].
Next, the data was aggregated to super county level. The data at super county level are person level attributes:

*Male

*Female

*Age 0-4

*Age 5-17

*Age 18-24

*Age 25-54

*Age 55+

<h6> Table: BEBR Super County Totals (2015) </h6>

{| class="wikitable"
|-
! SCOUNTY
! PERSONS
! MALE
! FEMALE
! AGE0-4
! AGE5-17
! AGE18-24
! AGE25-54
! AGE55+
|-
| 12109107
| 286,322
| 140,049
| 146,273
| 15,257
| 47,539
| 23,702
| 104,701
| 95,123
|-
| 12031000
| 905,574
| 440,059
| 465,515
| 61,304
| 146,443
| 87,517
| 379,741
| 230,569
|-
| 12003089
| 103,553
| 51,969
| 51,584
| 5,963
| 16,366
| 8,505
| 38,968
| 33,751
|-
| 12019000
| 201,277
| 98,400
| 102,877
| 12,543
| 37,662
| 18,453
| 79,447
| 53,172
|}

<h5> TAZ </h5>

The raw input data was downloaded at various levels [Source: 2011-15 American Community Survey five-year estimates, ACS5] and it was processed at TAZ level to generate household attributes:

*Households by Household Size at Block Group Level
**Household Size 1
**Household Size 2
**Household Size 3
**Household Size 4+

*Tenure by Age of Householder at Block Group Level
**Householder Age 15-44
**Householder Age 45-64
**Householder Age 65+

*Households by number of workers at Census Tract Level
**Zero household workers
**One household workers
**Two household workers
**Three or more than three household workers

*HHs by income at Census Tract Level
**Household income $0-$24,999
**Household income $25,000-$59,999
**Household income $60,000-$99,999
**Household income $100,000+

<h5> MAZ </h5>

The raw input data was obtained at parcel level for the year 2010 and calculated for 2015 using growth factor from 2010 to 2015. The data obtained is total number of households and then aggregated for each MAZ. Once the control files are prepared, the total number of households and persons are calculated across geographies to check for consistency as shown in Table below.

<h5> Table: Control data summary (2015) </h5>

{| class="wikitable" style="font-weight:bold; vertical-align:middle;"
|- style="text-align:center;"
! style="text-align:left;" | Data
! Geography
! Value
|-
| Total number of households
| style="text-align:center;" | MAZ
| style="font-weight:normal;" | 582,199
|-
| Total number of households
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 582,199
|-
| Number of households in HHSIZE=1
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 155,522
|-
| Number of households in HHSIZE=2
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 196,050
|-
| Number of households in HHSIZE=3
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 87,996
|-
| Number of households in HHSIZE=4+
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 110,821
|-
| Total number of persons (from HHSIZE)
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 1,397,535
|-
| Total number of persons
| style="text-align:center;" | SCOUNTY
| style="font-weight:normal;" | 1,397,534
|}

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev.jpg]][[File:Prmse.jpg]]

<h2> Development of 2045 land-use data </h2>

The 2045 land-use data was created by applying a methodology, detailed in the model documentation, to the land-use data for the base year (2015) and the BEBR forecasts. The table below shows the summary of comparisons between land-use data for the base year (2015) and the future year (2045).

<h4> Table: Comparison of 2015 and 2045 totals </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! Households<br />2015
! Households<br />2045
! Employment<br />2015
! Employment<br />2045
! Enrollment<br />2015
! Enrollment<br />2045
|-
| NASSAU
| 32,983
| 51,902
| 28,480
| 55,393
| 13,368
| 31,576
|-
| DUVAL
| 357,463
| 541,730
| 496,394
| 708,478
| 211,091
| 362,983
|-
| ST JOHNS
| 84,540
| 181,500
| 66,355
| 179,066
| 43,977
| 116,687
|-
| CLAY
| 70,523
| 132,830
| 46,539
| 105,158
| 40,877
| 98,998
|-
| BAKER
| 8,815
| 15,117
| 8,909
| 18,491
| 5,128
| 9,876
|-
| PUTNAM
| 31,278
| 40,307
| 21,521
| 35,564
| 14,856
| 23,404
|- style="font-weight:bold;"
| TOTAL
| 585,602
| 963,386
| 668,198
| 1,102,150
| 329,297
| 643,524
|}

<h2> Development of 2045 control data </h2>

Controls at three levels of geography are required to run PopulationSim for North Florida TPO: Super County, TAZ and MAZ. The Super COUNTY level controls were obtained from BEBR data and the TAZ and MAZ level controls were prepared using the 2045 land-use data. Please refer to the model documentation for the details of this development process.

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev2045.jpg|778x972px]]

<h2> Updating 2045 control data </h2>

When updating land-use data it is necessary to update the PopulationSim controls. The following example shows how to update the controls when adding 100 households to MAZ 1439:

<h4> Land-use file before update </h4>
[[File:lubefore45.jpg]]

*Update the land-use “nftpo_microzones_2045.csv” file by adding 100 households to MAZ 1439. The figure above shows the land-use file before the addition of 100 households and the figure below shows the land use file after the addition of 100 households.

<h4> Land-use file after update </h4>
[[File:luafter45.jpg]]

*Edit the variables listed in the table below within the CUBE script “update_controls.S” located in “./User.prg/Population_Synthesis/” folder. The script performs following actions:
**Makes a copy of control files.
**Updates MAZ level household controls by comparing existing control values with land-use values.
**Updates TAZ level household controls by aggregating land-use values by TAZ. Redistributes the additional households by household size based on the distribution of households by household size as observed in control data. Similarly redistributes additional household based on distribution of households by householder age, number of household workers, and household income respectively as observed in the control data.
**Updates County level population controls by adding additional person based on average household size calculated for each county from existing controls. The additional population is distributed across gender and age group based on distribution of population observed in controls for respective categories.

<h4>Table: Variables to update by user when updating update_control.S script </h4>

{| class="wikitable"
|-
! VARIABLE NAME
! DESCRIPTION
! EXAMPLE
|-
| LANDUSE_PATH
| Full path to the folder that contains the land-use file.
| 'E:\NERPMABv2.0\User.prg\DaySim_Data_Tools'
|-
| LANDUSE_FILE
| Name of the land-use file
| 'nftpo_microzones_2045.csv'
|-
| POPSIM_PATH
| Full path to the PopulationSim folder
| 'E:\NERPMABv2.0\User.prg\Population_Synthesis'
|-
| CONTROL_FOLDER
| Name of the folder containing the control data
| 'data_2045'
|-
| OVERWRITE_INITIAL
| A value of 0 will not overwrite the initial files and a value of 1 will overwrite the initial files.
| 0: Does not overwrite the control_totals_{zone}_initial.csv file<br />1: Overwrites the control_totals_{zone}_initial.csv file
|}

*Run “update_controls.bat” file, located in “./User.prg/Population_Synthesis/” folder, by double-clicking. This will call the Cube Voyager and run the “update_controls.S” script.
*The control files are now updated and are ready to be used by PopulationSim.

<h2> Development of 2030 land-use data </h2>

The 2030 land-use data was created by applying a methodology, detailed in the model documentation, to the land-use data for the base year (2015) and the BEBR forecasts. The table below shows the summary of comparisons between land-use data for the base year (2015) and the future year (2030).

<h4> Table: Comparison of 2015 and 2030 totals </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! Households<br />2015
! Households<br />2030
! Employment<br />2015
! Employment<br />2030
! Enrollment<br />2015
! Enrollment<br />2030
|-
| NASSAU
| 32,983
| 41,948
| 28,480
| 45,394
| 13,368
| 25,998
|-
| DUVAL
| 357,463
| 453,184
| 496,394
| 622,922
| 211,091
| 311,765
|-
| ST JOHNS
| 84,540
| 135,295
| 66,355
| 124,742
| 43,977
| 79,721
|-
| CLAY
| 70,523
| 100,980
| 46,539
| 81,119
| 40,877
| 75,838
|-
| BAKER
| 8,815
| 12,212
| 8,909
| 14,615
| 5,128
| 7,602
|-
| PUTNAM
| 31,278
| 35,535
| 21,521
| 32,993
| 14,856
| 22,076
|- style="font-weight:bold;"
| TOTAL
| 585,602
| 779,154
| 668,198
| 921,785
| 329,297
| 523,000
|}

<h2> Development of 2030 control data </h2>

Controls at three levels of geography are required to run PopulationSim for North Florida TPO: Super County, TAZ and MAZ. The Super COUNTY level controls were obtained from BEBR data and the TAZ and MAZ level controls were prepared using the 2030 land-use data. Please refer to the model documentation for the details of this development process.

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev2030.jpg|778x972px]]

<h2> Updating 2030 control data </h2>

When updating land-use data it is necessary to update the PopulationSim controls. The following example shows how to update the controls when adding 100 households to MAZ 1439:

<h4> Land-use file before update </h4>
[[File:lubefore45.jpg]]

*Update the land-use “nftpo_microzones_2030.csv” file by adding 100 households to MAZ 1439. The figure above shows the land-use file before the addition of 100 households and the figure below shows the land use file after the addition of 100 households.

<h4> Land-use file after update </h4>
[[File:luafter45.jpg]]

*Edit the variables listed in the table below within the CUBE script “update_controls.S” located in “./User.prg/Population_Synthesis/” folder. The script performs following actions:
**Makes a copy of control files.
**Updates MAZ level household controls by comparing existing control values with land-use values.
**Updates TAZ level household controls by aggregating land-use values by TAZ. Redistributes the additional households by household size based on the distribution of households by household size as observed in control data. Similarly redistributes additional household based on distribution of households by householder age, number of household workers, and household income respectively as observed in the control data.
**Updates County level population controls by adding additional person based on average household size calculated for each county from existing controls. The additional population is distributed across gender and age group based on distribution of population observed in controls for respective categories.

<h4>Table: Variables to update by user when updating update_control.S script </h4>
{| class="wikitable"
|-
! VARIABLE NAME
! DESCRIPTION
! EXAMPLE
|-
| LANDUSE_PATH
| Full path to the folder that contains the land-use file.
| 'E:\NERPMABv2.0\User.prg\DaySim_Data_Tools'
|-
| LANDUSE_FILE
| Name of the land-use file
| 'nftpo_microzones_2030.csv'
|-
| POPSIM_PATH
| Full path to the PopulationSim folder
| 'E:\NERPMABv2.0\User.prg\Population_Synthesis'
|-
| CONTROL_FOLDER
| Name of the folder containing the control data
| 'data_2030'
|-
| OVERWRITE_INITIAL
| A value of 0 will not overwrite the initial files and a value of 1 will overwrite the initial files.
| 0: Does not overwrite the control_totals_{zone}_initial.csv file<br />1: Overwrites the control_totals_{zone}_initial.csv file
|}

*Run “update_controls.bat” file, located in “./User.prg/Population_Synthesis/” folder, by double-clicking. This will call the Cube Voyager and run the “update_controls.S” script.

*The control files are now updated and are ready to be used by PopulationSim.

<h2> DaySim ABM Model Update </h2>

After completion of base year 2015 data aggregation from parcels to microzones, DaySim was tested for the new geographies and the results compared against the parcel results to check for consistencies. The necessary input files were created for DaySim run as explained in detail in Appendix B of the model documentation. After a successful run of DaySim using updated 2015 Microzone and Population synthesis dataset, the results were compared with previous DaySim run using parcels. The small set of tables and figures displayed here show the consistency of the 2015 dataset in DaySim performance. A complete series of tables and figures are shown in the model documentation.

<h4> Table: Estimated Households by Number of Vehicles and County (Parcel) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 309
| 2,750
| 3,899
| 1,285
| 557
| 8,800
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 2%
|-
| Clay
| 2,154
| 19,501
| 32,064
| 11,436
| 4,123
| 69,278
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 12%
|-
| Duval
| 27,194
| 126,487
| 137,367
| 42,231
| 15,633
| 348,912
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 61%
|-
| Nassau
| 1,212
| 10,006
| 14,514
| 4,681
| 1,820
| 32,233
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 6%
|-
| Putnam
| 1,594
| 11,944
| 12,800
| 3,767
| 1,543
| 31,648
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 6%
|-
| St. Johns
| 3,097
| 25,256
| 36,209
| 11,566
| 4,102
| 80,230
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 14%
|- style="font-weight:bold;"
| Total
| 35,560
| 195,944
| 236,853
| 74,966
| 27,778
| 571,101
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 100%
|}

<h4> Table: Estimated Households by Number of Vehicles and County (Microzone + Node to Node Distances) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 333
| 2,781
| 3,868
| 1,261
| 557
| 8,800
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 2%
|-
| Clay
| 2,491
| 19,945
| 31,772
| 11,122
| 3,948
| 69,278
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 12%
|-
| Duval
| 28,867
| 126,963
| 136,315
| 41,659
| 15,108
| 348,912
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 61%
|-
| Nassau
| 1,329
| 10,195
| 14,426
| 4,515
| 1,768
| 32,233
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 6%
|-
| Putnam
| 1,627
| 11,994
| 12,767
| 3,750
| 1,510
| 31,648
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 6%
|-
| St. Johns
| 3,795
| 25,835
| 35,721
| 11,105
| 3,774
| 80,230
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 14%
|- style="font-weight:bold;"
| Total
| 38,442
| 197,713
| 234,869
| 73,412
| 26,665
| 571,101
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 100%
|}

<h4> Table: Estimated Households by Number of Vehicles and County (Microzone + N2N Distances + PopSim 2015) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 403
| 3,021
| 3,704
| 1,015
| 672
| 8,815
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 2%
|-
| Clay
| 2,742
| 21,083
| 31,758
| 11,032
| 3,908
| 70,523
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 12%
|-
| Duval
| 29,793
| 131,763
| 131,610
| 44,856
| 16,038
| 354,060
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 61%
|-
| Nassau
| 1,471
| 12,051
| 13,617
| 3,469
| 2,375
| 32,983
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 6%
|-
| Putnam
| 2,158
| 13,690
| 10,852
| 3,059
| 1,519
| 31,278
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 5%
|-
| St. Johns
| 3,652
| 28,418
| 37,278
| 11,588
| 3,604
| 84,540
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 15%
|- style="font-weight:bold;"
| Total
| 40,219
| 210,026
| 228,819
| 75,019
| 28,116
| 582,199
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 100%
|}

<h4>Work Location Choice: HOME-WORK DISTANCES </h4>

[[File:WLCHWD.JPG|800x331px]]

<h4>School Location Choice: HOME-SCHOOL DISTANCES </h4>

[[File:SLCHSD.JPG|800x331px]]

<h2>Development of External Model Inputs </h2>

<h3>Data Sources </h3>

The source data for the X-X trip table has been obtained from Bluetooth detectors that were installed at 17 locations adjacent to highways at external model stations. This work was conducted under a separate contract with Florida DOT. The raw data from the Bluetooth deployment were obtained by HNTB and processed for further analysis by RSG.

*2010 North Florida TPO model X-X trip table (EETRIPS.dbf): (29 nodes)
The total trips originating for each external node and destined for other external nodes are calculated by assigning a proportion of the total incoming traffic (e.g. 16%). Incoming traffic is distributed from each origin external node to each other external node in proportion to the destination node’s AADT.

*EE_O-D_Study spreadsheet provided by HNTB: (17 nodes)
This spreadsheet was provided by HNTB and contains the raw matched BT X-X data, total traffic counts, total truck counts, matched BT X-X/X-I data (17 external and 150 internal nodes), and related pivot tables.

<h3>Estimating the BT Expansion Factor </h3>

The BT expansion factor (or penetration rate) is the percentage of total traffic recorded with having a discoverable Bluetooth device on or within the vehicle. The expansion factors imputed from the HNTB data were abnormally high given RSG’s prior experience in working with data of this type. RSG requested, but never received from HNTB, the raw Bluetooth data. For this reason, RSG developed a method to estimate an expansion factor for each station. The details of the method can be found in the model documentation.

<h3>Estimating External-External Trips </h3>

The raw matched X-X BT data alongside with expansion factors are used to estimate the X-X trips. The data is cleaned based on methodology described in the model documentation.

<h2>Network Updates </h2>

<h3>Network Link Updates </h3>

The previous Existing + Committed (E+C) network, known as 2040A in the 2010 model setup, was used as the starting point for the 2015 network. Table below lists the previous E+C network links that were modified to be consistent with 2015 base year conditions. Modifications included revisions to the number of lanes, facility type, and turn restrictions.

UPDATE (01/30/2020): BUSTFAC_45C was set to 0 in the 2045 network fields. This was causing problems with transit assignment resulting in very low transit ridership. RSG set this to 1 for all links since BUSTFAC_15A has it set to 1 for all links. This resolved the low transit ridership issue in the scenario year 2045.

Before setting up a model run, please make sure that in the transit route input file (or any subsequent transit route input files), the skyway modes are coded as MODE equal 24 instead of 23 and commuter rail “CR_SE-Rail” is coded as MODE equal 25 instead of 27 to be consistent with the new in-vehicle time skims.

<h4>E+C Network (2040A) Links to Change for 2015 Network</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! County
! Route
! From
! To
|-
| Nassau
| Chester Rd.
| SR A1A
| Green Pine Rd.
|-
| Duval
| First Coast Expwy. (SR 23)
| Clay CL
| Argyle Forest Blvd.
|-
| Duval
| First Coast Expwy. (SR 23)
| Argyle Forest Blvd
| I-10
|-
| Clay
| First Coast Expwy. (SR 23)
| Blanding Blvd (SR 21)
| Duval CL
|-
| Duval
| I-10 @ I-95
| Roosevelt Ave. (US 17)
| San Marco Ave.
|-
| Duval
| I-295
| SR 9B
| J.T. Butler Blvd. (SR 202)
|-
| Duval
| I-295
| I-10
| Commonwealth Ave.
|-
| Duval
| I-295
| Buckman Bridge
| I-95
|-
| Duval
| I-295
| I-95 (South)
| SR 9B
|-
| Duval
| Martin Luther King Jr. Pkwy
| at 21st St./Talleyrand Ave.
|
|-
| Duval
| SR 9B
| Philips Hwy. (US1)
| I-295
|-
| Duval
| SR 9B
| I-95
| Philips Hwy. (US1)
|-
| Duval
| US 301 (SR 200)
| South of Baldwin
| North of Baldwin
|-
| Duval
| Blanding Blvd/SR 21
| South of Old Jennings
| North of CR 218
|-
| Duval
| I-10 @ Beaver Street
| Remove access to Beaver Street
|
|-
| Nassau
| US 301 (SR 200)
| Duval CL
| Callahan
|}

<h3> Volume Count Updates </h3>

The North Florida TPO provided AADT counts from 2015 FDOT portable traffic monitoring sites at point locations throughout the network. Each count was for total vehicles and did not include directionality or time of day factors. It was assumed that counts on bidirectional roads were equally split between both directions.

These points were spatially joined to the network links and then spot checked to ensure the join was accurate. Particular attention was paid to on and off ramps as they tended to have the least accuracy.

The NERPM-AB has four time periods: AM, midday (MD), PM, and night (NT). The 2010 network contains count data for each of these periods on some links. To create time of day count data for the 2015 counts, the 2015 count was distributed between the four time periods using the same ratio as the 2010 counts. Links with a 2015 count but no 2010 time of day counts used nearby 2010 time of day counts. If no 2010 count was nearby, that 2015 count was not used.

<h2>CUBE Model Update </h2>

Several updates were made to the NERPM-AB Cube model during this project. The previous version of the model had model steps and scripting logic that was left over from the previous four-step model. Therefore, enhancements were made wherever necessary to make sure that the model system and results appear reasonable.

<h3>External-Internal Trips Calibration</h3>

While looking at auxiliary model demand it was found that IE trips are very long – average VMT per trip is approximately 32 miles, compared to 8 miles for internal trips. The VMT needed to be reduced by factoring either or both total IE or truck trips, or by factoring the length of IE trips for which the gravity model used to distribute them needed adjustment. To help inform this decision, the relationship between the observed counts and the estimated link volumes at external stations was investigated. It was found that both ends of I-95 and on I-10 were low, IE trips needed to be increased. It was also possible that the IE trips were traveling too far, which could be adjusted by making the friction factor curve used in trip distribution for IE trips steeper. A series of steps were taken to resolve this issue as discussed in the model documentation.

<h3>Transit Skimming Updates</h3>

From the initial transit assignment results, it was found that the skyway/monorail boardings were significantly lower than observed boardings. Therefore, the next model enhancement made was to add alternative-specific constants (ASC) to DaySim for transit modes based on an in-vehicle time skim. For the transit alternatives that need to be modeled in the scenarios, the in-vehicle time (IVT) needs to be skimmed in a separate skim matrix, and any tour or trip that has the transit mode in the OD Pair should get the constant. The updates are discussed in detail in the model documentation.

<h3>Highway Skimming for Tolls</h3>

In the E+C 2045 networks, express lanes or managed lanes were introduced in the highway network. Initially the toll variable, CARTOLL, was set at zero for all links. This was changed to 20 cents per mile (0.20). This value (CARTOLL) was then multiplied with the link length throughout the model setup to calculate the cost. Moreover, the occupancy for HOV2 and HOV3+ were also changed to 1.3 and 1.5, respectively.

<h2>DaySim Model Calibration and Validation</h2>

The tour destination and other sub models were calibrated using the North Florida Household Travel Survey (2017), the transit tours and trips were calibrated using the Transit On-Board Survey (2016) obtained from Transit On-Board Survey Program, and the worker flow were calibrated using CTPP (2010) obtained from Census.

The following set of tables and figures show calibration summaries:

<h4>Table: Population by Person Type</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! Person type
! Survey
! PopSim
|-
| Full Time Worker
| 540,570
| 566,852
|-
| Part Time Worker
| 96,726
| 59,179
|-
| Retired
| 172,447
| 142,992
|-
| Non-Worker
| 203,794
| 208,490
|-
| University Student
| 43,943
| 55,998
|-
| Student Age 16+
| 37,901
| 48,543
|-
| Student Age 5-15
| 185,288
| 197,734
|-
| Kid under 5
| 83,362
| 102,243
|- style="font-weight:bold;"
| Total
| 1,326,484
| 1,419,578
|}

<h4>Table: Average Home to Work Distance</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! Worker Type
! Survey
! DaySim
|-
| Full Time
| 13.7
| 13.3
|-
| Part Time
| 9.1
| 6.8
|-
| Other
| 9.9
| 14.5
|- style="font-weight:bold;"
| Total
| 13.2
| 12.4
|}

<h4>Table: Workers by County</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! CTPP
! HTS
! DaySim
|-
| 1
| 7,283
| 5,383
| 9,681
|-
| 2
| 59,391
| 80,418
| 84,565
|-
| 3
| 286,647
| 400,012
| 420,057
|-
| 4
| 18,556
| 17,424
| 32,801
|-
| 5
| 13,779
| 10,901
| 20,362
|-
| 6
| 64,325
| 79,338
| 97,402
|- style="font-weight:bold;"
| TOTAL
| 449,980
| 593,476
| 664,868
|}

<h2> Model Calibration and Validation </h2>

The 2017 north florida travel survey and the 2016 onboard rider demographic survey were used to calibrate the model. Traffic counts and transit boarding counts were used to validate the model outputs. The following tables and figures show the results of the calibrated model. These include trip length frequency distributions, tour and trip mode choice model results, highway and transit validation etc.

<h4> Table: Population and Employment </h4>

{| class="wikitable"
|-
! County
! 2015 Population
! 2015 Employment
! BEA 2015
! 2030 Population
! 2030 Employment
! 2045 Population
! 2045 Employment
|-
| Baker
| 23,138
| 9,649
| 9,695
| 32,340
| 15,876
| 37,723
| 20,123
|-
| Clay
| 189,600
| 68,871
| 69,317
| 263,882
| 120,166
| 321,984
| 156,073
|-
| Duval
| 854,757
| 624,952
| 623,596
| 1,078,136
| 786,052
| 1,231,564
| 888,333
|-
| Nassau
| 76,672
| 30,948
| 31,086
| 97,500
| 49,367
| 116,024
| 60,801
|-
| Putnam
| 71,687
| 22,605
| 22,780
| 78,328
| 34,695
| 84,790
| 38,102
|-
| St Johns
| 202,375
| 105,077
| 104,983
| 318,041
| 197,421
| 412,811
| 287,415
|-
| Grand Total
| 1,418,229
| 862,102
| 861,457
| 1,868,227
| 1,203,577
| 2,204,896
| 1,450,847
|}

<h4>Trip Length Frequency Distribution </h4>
[[File:Wsloc.JPG]]

<h4>Tour Mode Choice </h4>
[[File:Tourmode.JPG]]

<h4>Trip Mode Choice </h4>
[[File:Tripmode.JPG]]

<h4>Highway Validation </h4>
[[File:Hwyvalidation.JPG]]

<h4>Transit Validation </h4>
[[File:Trnvalidation.JPG]]

<h2>Software Requirements</h2>

PopulationSim requires the following software, already included in the model setup, and can be found in the following location w.r.t the project folder:

*Anaconda2 with Python 2.7 – “.\User.prg\Population_Synthesis\Anaconda2”

<h2>Setup and Run PopulationSim</h2>

<h3>Setup Directory</h3>

Figure below presents the directory structure for the PopulationSim setup. To set up a PopulationSim run, the user must create the directory structure as shown in below.

<h4>PopulationSim Directory Structure</h4>
[[File:PopSimDir.JPG]]

The folders and files in the directory are explained as follows:

*The data directory holds all the input data like seeds data, control data and crosswalks.
**SCOUNTY Control totals
**TAZ Control totals
**MAZ Control totals
**Geographic Crosswalk
**Household Seed table
**Person Seed table

*The Anaconda2 directory houses the core PopulationSim software files and associated libraries and packages.

*The configs folder contains the settings.yaml file and controls.csv file
**PopulationSim is configured using the settings.yaml file. For this project, it is configured to run in base mode which means it is run from beginning to end and produces a new synthetic population
**controls.csv file specifies all the targets, geography, seed table, control field and their expression to the seed table required for the PopulationSim run.

*The output folder will have the final synthetic household and person file and summary attributes after a successful run.

*This batch file activates the PopulationSim environment and then calls the run_populationsim.py Python script to launch a PopulationSim run.

*PopulationSim is run using the RunPopulationSim.bat batch file in Command Prompt Window. Before starting the run, the path to the Anaconda install must be updated within this file.

The PopulationSim procedure is automated and once you have all the data in place, the user just needs to run the batch file from the command prompt window. To run the PopulationSim for 2030 or 2045 the user need to update the ‘data_dir’ field in the settings.yaml file. The ‘data_dir’ field for different scenario years are:

*2015: The ‘data_dir’ in settings.yaml file should be set to ‘data’

*2030: The ‘data_dir’ in settings.yaml file should be set to ‘data_2030’

*2045: The ‘data_dir’ in settings.yaml file should be set to ‘data_2045’

<h2>Preparing DaySim Inputs</h2>

<h3>Transit Files</h3>

The transit stops file is created using the input transit network from Jacksonville Transit Authority: TROUTE_{Year}{Alt}.LIN
Follow the sequential step below:

*Create a geodatabase (gdb) in Cube

*Import the input transit network file and the highway network file

*Export the nodes (PTNetwork_PTNode) to a shapefile

*Open the shapefile in ArcMap and select the nodes that have STOPNODE=1. These are the transit stops

*Export the selected nodes (stops) to a new stops shapefile. Also Export the transit line file to a shapefile.

*Open the attribute table of the new stops file and add four fields: xcoord_p (double), ycoord_p (double), mode (int) and id (long).

*Calculate xcoord_p and ycoord_p using calculate geometry for the two fields.

*Join transit line by object id and calculate a new field “mode” equal to the MODE field in the line file (1-localbus, 2-skyway). Jacksonville mode codes and daysim mode codes are different, convert these to daysim mode codes before running buffer tools.

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! 2015
! 2030
! 2045
! DaySim
|-
| 21 = LB
| 21 = LB
| 21 = LB
| 1 = LB
|-
| 24 = Skyway (coded as LB)
| 23 = Skyway (coded as LB)
| 23 = Skyway (coded as LB)
| 2 = EB
|-
|
| 27 = CR
| 27 = CR
| 3 = CR
|}

*Calculate stopid as “FID+1”.

*Keep only four fields in the attribute table: id, mode, x_coord, y_coord.

*Export all records in the attribute table to a csv file: nftpo_transitstops.csv

<h2>DaySim Data Editing</h2>

With the base year moving from 2010 to 2015, microzones replacing parcels and new population data, the inputs for DaySim needed to be updated. The following steps describe the DaySim input data preparation and update for the NERPM-AB model including the tool and input data used and output formed.

<h3>Network Data Preparation</h3>

This step calculates “nearby” node pairs of microzones for shortest distance path calculations to be used in DaySim.

Tool: '''Network_DataPrepv2.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*input_node.csv (Node x,ys from an all-streets network)
*jax_MAZs_2015.dat (The coordinates of the newly developed microzones)
*jax_netprep.ctl (Network prep control file)

Output:
*input_od_pairs.csv (for input to shortest path update tool)

<h3>Shortest Path Update</h3>

This process is required to generate more accurate short distances based on an all streets network. DTALite, a dynamic traffic assignment software, is used to generate node-to-node shortest path distances using the all streets network.

Tool: '''DTALite64.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*input_od_pairs.csv (from the Network Data Preparation tool)
*input_node.csv (from all-street network)
*input_link_type.csv (from all-street network)
*input_link.csv (from all-street network)
*DTASettings.ini (settings file)

Output:
*output_shortest_path.txt (for input to Buffering microzones)

<h3>Buffering micro-zones</h3>

The Daysim buffering tool is run to prepare MAZ input file for DaySim. This step calculates the new Microzone buffer measures to be used in DaySim.

Tool: '''DSBuffTool.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*Jax_microzones_2015.csv (Base Microzone file)
*Jacksonville_Intersections.csv (Street intersections file)
*Jacksonville_transitstops.csv (Transit stops file)
*Jacksonville_openspaces.csv (Open spaces/parks file)
*input_node.csv (All-street Network nodes file)
*output_shortest_path.txt (Node-to-node shortest path distance file)

Output:
*buffered_microzone_2015.dat (to be used in DaySim)
*microzonenode.dat (to be used in DaySim)
*output_shortest_path.txt.bin (Change extension using batch file)
*output_shortest_path.txt.index (Change extension using batch file)

The file extension of the last two outputs above need to be changed for reading into DaySim. The last three output files are used in DaySim to estimate short-distances for car, walk and bike trips.

All three DaySim Data tools can be run using RunAll.bat from the command prompt window.

<h3>Running DSBuffTool.exe</h3>

Double-clicking the executable will bring up a GUI as shown in the following figure. The use of this tool is straightforward. The user just needs to specify all the inputs and click on the “Run” button at the bottom of the GUI.

<h4>DaySim Buffering Tool GUI</h4>
[[File:DBTG.JPG]]

In the “INPUT” section of the GUI, distance calculation and buffer type are set to “Euclidean” and “Logistic decay” respectively. These are the recommended settings for running this tool. If node-to-node distances obtained from an all streets network are available, distance calculation may be set to “Node-to-Node”. The default recommended parameters for logistic decay weights (described in the model design chapter) such as buffer decay slope, offset etc., are also automatically populated in the appropriate fields in the GUI.

All other inputs are to specify file paths that can be done by clicking on the “Browse” button. This pops a file dialog as show in the figure below. The user may also enter full file paths manually using keyboard.

<h4>Buffer Tool File Selection Dialog</h4>
[[File:BTFSD.JPG]]

All file specification fields for the tool are described below. Details about formats for the input files can be found in the directory and data structures chapter.

*MAZ Data File: This is the base MAZ file. It is obtained by running the disaggregation tool.

*Intersection Data File: This file has the coordinates of intersections and the number of links intersecting at each one of them.

*Transit Stops File: This file has the transit stop location coordinates by transit sub-mode.

*Open Spaces File: This file has the locations and area of parks/open spaces in the model region.

*Circuity Data File: This is only required for “Circuity” distance calculation that is now an obsolete method.

When “Node-to-Node” distance calculation is selected, the user need to select a format of the input node-node distance file: output of DTALite or input file used in DaySim. The following files are only needed for “Node-to-Node” distance calculation method.

*Node Data File: This file contains the coordinates of node from an all streets network.

*Node to Node Distance File: This file contains the network shortest path distances between a list of node pairs that are within 3 miles of each other. It is either an output from DTALite or a distance file input to DaySim.

*MAZ-Node Correspondence File: This is an intermediate file created during the buffering process and contains the correspondence between MAZs and nodes.

*Open spaces-Node Correspondence File: This is an intermediate file created during the buffering process and contains the correspondence between open spaces and nodes.

Specification of the following XML files is optional. However, they store the input configuration of a buffering tool run and make it convenient to use the tool.

*Output XML File: If a file is specified in this field, an XML file containing all the input information specified by the user is stored upon clicking on “Run” button that initiated the buffer process.

*XML Input File: Once an XML file has been stored as described above, it can be selected to automatically populate all the input fields required for a buffer run. This can be used to re-run a buffer process with the same inputs or modify a few inputs to generate a different buffered MAZ file.

Finally, Buffered Output File field is used to specify the location and name of the buffered microzone output file that needs to go into DaySim inputs folder.

<h3>DaySim Files Update</h3>

The following files were updated in the latest 2015 DaySim run:

*New Microzone file containing aggregated household and employment inputs and buffered measures instead of parcels

*Household file containing all household information with respect to the microzones

*DaySim Configuration file containing the DaySim execution settings

*A set of updated DaySim input files

*A set of new Data files for short distance calculation

To accurately calculate short car, walk and bike trips, short distance node-to-node measure is introduced in DaySim. This includes the following changes:

*Change in DaySim Configuration file

*Utilization of three additional files in DaySim:
**Microzone node file (microzonenode.dat)
**Node Index file (output_shortest_path_txt_index.dat)
**Node distance file (output_shortest_path_txt_bin.dat)

<h3>Activating TAZ</h3>

The model contains a list of TAZs that are dummy TAZs that act as a placeholder to be later used when building new TAZs. The steps to utilize or activate these dummy zones is as follow:

*Identify the dummy TAZ that needs to be activated.

*Update the input network MicroCodedHnet42.net within Cube.
**Relocate the dummy TAZ to the centroid of new TAZ.
**Update the attributes of the dummy TAZ.
**Build network links that would connect the dummy TAZ to appropriate nodes on the network.
**Update the attributes of these network links.

*Identify the overlap/association between existing MAZs and the new TAZ.

*Update the land-use file nftpo_microzones_year.csv.
**Update the “taz_p” field for MAZs that are now identified as associated with new TAZ for all scenario years.

*Update the PopulationSim Inputs.
**Update the fields associated with MAZs identified in the earlier step with updated TAZ in geo_cross_walk.csv.
**Update the TAZ field for identified MAZs in control_totals_maz.csv file.
**Update the control_totals_taz.csv file by adding a row for dummy TAZ and recording appropriate values in rest of the fields. It should be ensured that the values entered are reasonable for e.g. household size values should not all be 0 and should match up to total households.
**Go through the steps described in Updating Land-use Manually later in this document.

*Update scenario input files in “scenario/Input/” folder.
**Add/Update the “Zone_id” and “Zone_ordinal” field in the _jax_taz_indexes.dat file located in “scenario/Input/DaySimInput/01_TAZ_Index/” folder to the dummy TAZ. Set the “Dest_eligible” field for this TAZ to a value of 1.
**Add/Update the TAZ field in the emp_year.dbf file located in “scenario/Input/DaySimInput/02_Parcel/” folder.
**Add/Update the TAZ field in the _jax_worker_ixxifractions.dat file located in “scenario/Input/DaySimInput/05_ixxi/” folder.
**Add/Update the TAZ field in the county_districts.csv file located in the “scenario/Input/DaySimInput/08_Summaries/” folder.
**Add/Update the TAZ field in the RIVERCROSS.csv file located in the “scenario/Input/” folder.

This concludes the steps needed to activate a dummy TAZ, and the use can now run the regular model.

<h3>Updating Land-use Manually</h3>

When updating land-use data, like household or population attributes, for the base or future year, the steps below should be followed to ensure proper propagation of changes throughout the model:

*The starting point for such changes is the buffering micro-zones step. One of the inputs to the buffering tool is the MAZ file, for example, nftpo_microzones_2015.csv for the 2015 scenario. The user should manually update the land-use file for changes required in specific zones because of new development, changed land-use, etc. The user should then run the buffer tool and copy the output file (buffered_maz_2015.dat) to the scenario input directory.
*The next step is to update the PopulationSim controls by following the guidelines in Section 3.1 for the base year or in Appendix A (Updating Control Data). The user should then run PopulationSim.

*The user should then run the “popsim_to_daysim.bat” file by double clicking it in Windows or running it from a DOS command promt. The batch file runs the “convert.py” script that converts PopulationSim output to DaySim inputs. The DaySim inputs are output in the “./User.prg/PopulationSim/popsim_to_daysim/output/” folder. The output should be renamed as shown in Table 78. These outputs should be copied to the model scenario input directory.

<h4>Table: Rename popsim_to_daysim.bat output</h4>

{| class="wikitable" style="font-weight:bold; text-align:center; vertical-align:middle;"
|-
! rowspan="2" | Output File
! colspan="3" | Rename File
|-
| Base2015
| INT2030
| CF2045
|- style="font-weight:normal; text-align:left;"
| household_2015.dat
| household_2015.dat
| household_2030.dat
| household_2045.dat
|- style="font-weight:normal; text-align:left;"
| person_2015.dat
| person_2015.dat
| person_2030.dat
| person_2045.dat
|}

This concludes the land-use updates step, and the user can now run the regular model.

File:June2020 Training NERPM-AB 2.0.pdf

2021-01-27T19:29:48Z

Bstabler: Bstabler uploaded a new version of File:June2020 Training NERPM-AB 2.0.pdf

File:June2020 Training NERPM-AB 2.0.pdf

2021-01-27T19:26:57Z

Bstabler: Bstabler uploaded a new version of File:June2020 Training NERPM-AB 2.0.pdf

File:NERPM AB FinalReport v7.docx

2021-01-26T13:06:31Z

Bstabler: Model document for NERPMABv2.1

Model document for NERPMABv2.1

7.3 Version NERPMAB2

2021-01-26T13:05:17Z

Bstabler: Added link to model document

[[Category:7.0 Version Updates]]
'''7.3 VERSION NERPM-AB2'''

This section documents the set of improvements that were made to the NERPM model for the release of NERPM-AB2. The improvements focused on:

*Base year updated from 2010 to 2015
*Re-built all land use inputs for 2015 and future
*Switched from parcels to microzones to ease data preparation
*Switched from PopGen to PopulationSim
*Updated the highway network to 2015 (NFTPO)
*Updated the transit network to 2016 (JTA)
*Updated external traffic volumes and distributions based on Bluetooth OD data (FDOT)
*Upgraded from the NFTPO specific version of DaySim to the multiple-agency (a.k.a. core) version, which is faster, more stable, and includes new features such as Transportation Network Companies (TNC) mode (i.e. Uber and Lyft)
*Updated future year scenarios

The details of these improvements can also found in [[Media:NERPM_AB_FinalReport_v7.docx|NERPM_AB_FinalReport_v7.docx (DEC 2020)]].

<h2> Parcel to Microzone (MAZ) </h2>

In the previous version of the model, the unit of spatial analysis was parcels. There were approximately 700,000 parcels in the region, which made it difficult to maintain, edit and attribute, both the existing and alternative scenarios. This led to the creation and use of microzones as an alternative, which is halfway between parcels and TAZs and are easier to maintain. As you can see in the table, there are around 55 thousand microzones against 700 thousand parcels. This makes it much easier for the user to update land use information such as employment, enrollment etc. The total number of TAZs in this version is 1862. The plot shows the TAZ and the MAZ layer around downtown Jacksonville. This shows how TAZs and MAZs are related. You can see in the plot that MAZs in downtown tend to be smaller than the ones in the periphery. If you have any questions about the development of MAZs, please refer to the model documentation for more details.
{| class="wikitable"
|-
! <br />County
! <br />Number of Parcels
! <br />% Freq
! <br />Number of MAZ
! <br />% Freq
! <br />Number of TAZ
! <br />% Freq
|-
| <br />Baker
| <br />12,490
| <br />1.78
| <br />1,735
| <br />3.14
| <br />29
| <br />1.56
|-
| <br />Clay
| <br />84,529
| <br />12.02
| <br />7,796
| <br />14.13
| <br />184
| <br />9.88
|-
| <br />Duval
| <br />355,805
| <br />50.59
| <br />28,263
| <br />51.22
| <br />1,281
| <br />68.80
|-
| <br />Nassau
| <br />47,443
| <br />6.75
| <br />4,455
| <br />8.07
| <br />108
| <br />5.80
|-
| <br />Putnam
| <br />102,053
| <br />14.51
| <br />6,652
| <br />12.05
| <br />44
| <br />2.36
|-
| <br />St. Johns
| <br />100,950
| <br />14.35
| <br />6,283
| <br />11.39
| <br />216
| <br />11.60
|-
| <br />Total
| <br />703,270
| <br />100.00
| <br />55,184
| <br />100.00
| <br />1,862
| <br />100.00
|}

<h4> Figure: MAZ and TAZ layer in Downtown Jacksonville </h4>

[[File:Maztaz.jpg]]

<h2> Land-use data updated to base year 2015 </h2>

Once the microzones were formed the following land-use data were updated for the base year 2015:

<h3> Household Data </h3>

The household data at parcel level was aggregated to microzone level for the base year 2015.

<h3> Employment Data </h3>

The employment data was updated using three sources of data:

*The 2010 parcel employment data projected to 2015

*Data provided by Infogroup and edited by North Florida TPO

*Data provided by Cambridge Systematics as described in their June 30, 2017 memorandum “Development of 2015 Employment Data” for FDOT

Please refer to the model documentation for the details on how these data sources were utilized.

<h3> School Enrollment Data </h3>

School enrollment data from the Florida Department of Education (FDOE) provided by North Florida TPO was used to develop school enrollment data at microzone level for the base year 2015. The details of this development process can be found in the model documentation.

<h3> Parking </h3>

The parking data was derived from Florida DOT parking shapefiles "Parking surface" and "g100freeway". The website [https://www.bestparking.com/jacksonville-fl-parking/ Jacksonville, FL Parking] was also used along with the shapefiles to develop parking data.

<h3> Lodging </h3>

A list of 367 known hotels, motels, and bed and breakfasts from the Florida Department of Business and Professional Regulation provided by North Florida TPO was used to develop lodging information.

<h3> Special Generators </h3>

Jacksonville International Airport and St. Augustine are the two special generators that are effectively acting as a special generators in the model.

The Airport had an attraction value of 15,000 in 2010. Based on several sources, RSG determined that the passenger traffic at the airport has decreased slightly between 2010 and 2015. CAPA – Centre for Aviation noted an overall downward trend in traffic since 2008 but traffic began to pick up in 2013 . The airport’s website lists the 2010 and 2015 number of passengers as 5,602,000 and 5,502,000 respectively, which represents a 1.8% reduction in traffic. FDOT’s 2015 Airport Profile of JAX showed similar findings. Given the small change over the years, we recommend leaving the airport’s attraction value at 15,000 for 2015.

St. Augustine had an attraction value of 2,288 in 2010. This number accounts for tourists coming to the area. In 2010 and 2015, St. Johns County had 3.4 million and 6.3 million visitors, respectively, which represents an 85% growth over the five-year period. The 2015 number was considered an anomaly because it was the City’s 450th birthday. However, 2016 was even higher with 6.8 million visitors, so an 85% increase may be reasonable. Increasing the attractions value by 85% to 4,240 could have a noticeable impact on links near the TAZ.

<h2> PopulationSim </h2>

*PopulationSim is an open platform for population synthesis

*Replaced PopGen in this version of NERPM-AB

*Has better demographic and geographic methods compared to PopGen

*Actively maintained by the travel modeling community

*Run only when there are major changes in the landuse data

[http://https://activitysim.github.io/populationsim/# PopulationSim Wiki]

<h3> Input Data </h3>

The main inputs to a population synthesizer are

*Disaggregate population samples (Seed Sample)

*Marginal control distributions (Control variables)

The Seed sample is obtained from the [https://www.census.gov/programs-surveys/acs/data/pums.html Census Public Use Microdata Sample (PUMS)] and the Marginal distributions of person and household attributes are obtained from [https://www.bebr.ufl.edu/ Bureau of Economic and Business Research (BEBR)] and Census respectively.

The next step is the preparation of inputs to PopulationSim which includes:

<h4> Geographic cross-walk </h4>

PopulationSim can handle any number of nested geographies. The geography level (hierarchy) selected for NERPM-AB 2015 to which the marginal distributions are specified are as follows:

*Meta Geography: Super County (SCOUNTY)

*Seed Geography: PUMA

*Sub-seed Geography: TAZ and MAZ

Super-County is chosen instead of County due to overlapping of county and PUMA boundaries. Super County is the combination of multiple County to form one geographic area.

<h4> Seed population (Household and Person tables) </h4>

One of the main requirements for the seed sample is that it should be representative of the modeling region. The seed sample is obtained from PUMS dataset. The PUMS data contain five years of household records. The seed sample should be representative of the modeling region. It must contain all the specified control variables, as well as any variables that are needed for the travel model but not specified as controls. The PUMS data is downloaded from PUMS website and it is extracted both demographically and geographically for the North Florida TPO region using PUMA codes conforming to the region. There are 11 PUMA regions in the North Florida TPO area. The seed sample must include an initial weight field. The model setup includes the Seed sample that contains a weight field, WGTP, which is used for control of total households.

<h4> Controls </h4>

Controls or targets are the marginal distributions that form the constraints for the population synthesis procedure. The objective of the population synthesis procedure is to produce a synthetic population with attributes matching these marginal distributions. The controls were developed at three geographic levels:

<h5> Super County </h5>

The raw input data was downloaded at county level from [https://www.bebr.ufl.edu/sites/default/files/Research%20Reports/projections_2016_asrh.xlsx BEBR].
Next, the data was aggregated to super county level. The data at super county level are person level attributes:

*Male

*Female

*Age 0-4

*Age 5-17

*Age 18-24

*Age 25-54

*Age 55+

<h6> Table: BEBR Super County Totals (2015) </h6>

{| class="wikitable"
|-
! SCOUNTY
! PERSONS
! MALE
! FEMALE
! AGE0-4
! AGE5-17
! AGE18-24
! AGE25-54
! AGE55+
|-
| 12109107
| 286,322
| 140,049
| 146,273
| 15,257
| 47,539
| 23,702
| 104,701
| 95,123
|-
| 12031000
| 905,574
| 440,059
| 465,515
| 61,304
| 146,443
| 87,517
| 379,741
| 230,569
|-
| 12003089
| 103,553
| 51,969
| 51,584
| 5,963
| 16,366
| 8,505
| 38,968
| 33,751
|-
| 12019000
| 201,277
| 98,400
| 102,877
| 12,543
| 37,662
| 18,453
| 79,447
| 53,172
|}

<h5> TAZ </h5>

The raw input data was downloaded at various levels [Source: 2011-15 American Community Survey five-year estimates, ACS5] and it was processed at TAZ level to generate household attributes:

*Households by Household Size at Block Group Level
**Household Size 1
**Household Size 2
**Household Size 3
**Household Size 4+

*Tenure by Age of Householder at Block Group Level
**Householder Age 15-44
**Householder Age 45-64
**Householder Age 65+

*Households by number of workers at Census Tract Level
**Zero household workers
**One household workers
**Two household workers
**Three or more than three household workers

*HHs by income at Census Tract Level
**Household income $0-$24,999
**Household income $25,000-$59,999
**Household income $60,000-$99,999
**Household income $100,000+

<h5> MAZ </h5>

The raw input data was obtained at parcel level for the year 2010 and calculated for 2015 using growth factor from 2010 to 2015. The data obtained is total number of households and then aggregated for each MAZ. Once the control files are prepared, the total number of households and persons are calculated across geographies to check for consistency as shown in Table below.

<h5> Table: Control data summary (2015) </h5>

{| class="wikitable" style="font-weight:bold; vertical-align:middle;"
|- style="text-align:center;"
! style="text-align:left;" | Data
! Geography
! Value
|-
| Total number of households
| style="text-align:center;" | MAZ
| style="font-weight:normal;" | 582,199
|-
| Total number of households
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 582,199
|-
| Number of households in HHSIZE=1
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 155,522
|-
| Number of households in HHSIZE=2
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 196,050
|-
| Number of households in HHSIZE=3
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 87,996
|-
| Number of households in HHSIZE=4+
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 110,821
|-
| Total number of persons (from HHSIZE)
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 1,397,535
|-
| Total number of persons
| style="text-align:center;" | SCOUNTY
| style="font-weight:normal;" | 1,397,534
|}

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev.jpg]][[File:Prmse.jpg]]

<h2> Development of 2045 land-use data </h2>

The 2045 land-use data was created by applying a methodology, detailed in the model documentation, to the land-use data for the base year (2015) and the BEBR forecasts. The table below shows the summary of comparisons between land-use data for the base year (2015) and the future year (2045).

<h4> Table: Comparison of 2015 and 2045 totals </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! Households<br />2015
! Households<br />2045
! Employment<br />2015
! Employment<br />2045
! Enrollment<br />2015
! Enrollment<br />2045
|-
| NASSAU
| 32,983
| 51,902
| 28,480
| 55,393
| 13,368
| 31,576
|-
| DUVAL
| 357,463
| 541,730
| 496,394
| 708,478
| 211,091
| 362,983
|-
| ST JOHNS
| 84,540
| 181,500
| 66,355
| 179,066
| 43,977
| 116,687
|-
| CLAY
| 70,523
| 132,830
| 46,539
| 105,158
| 40,877
| 98,998
|-
| BAKER
| 8,815
| 15,117
| 8,909
| 18,491
| 5,128
| 9,876
|-
| PUTNAM
| 31,278
| 40,307
| 21,521
| 35,564
| 14,856
| 23,404
|- style="font-weight:bold;"
| TOTAL
| 585,602
| 963,386
| 668,198
| 1,102,150
| 329,297
| 643,524
|}

<h2> Development of 2045 control data </h2>

Controls at three levels of geography are required to run PopulationSim for North Florida TPO: Super County, TAZ and MAZ. The Super COUNTY level controls were obtained from BEBR data and the TAZ and MAZ level controls were prepared using the 2045 land-use data. Please refer to the model documentation for the details of this development process.

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev2045.jpg|778x972px]]

<h2> Updating 2045 control data </h2>

When updating land-use data it is necessary to update the PopulationSim controls. The following example shows how to update the controls when adding 100 households to MAZ 1439:

<h4> Land-use file before update </h4>
[[File:lubefore45.jpg]]

*Update the land-use “nftpo_microzones_2045.csv” file by adding 100 households to MAZ 1439. The figure above shows the land-use file before the addition of 100 households and the figure below shows the land use file after the addition of 100 households.

<h4> Land-use file after update </h4>
[[File:luafter45.jpg]]

*Edit the variables listed in the table below within the CUBE script “update_controls.S” located in “./User.prg/Population_Synthesis/” folder. The script performs following actions:
**Makes a copy of control files.
**Updates MAZ level household controls by comparing existing control values with land-use values.
**Updates TAZ level household controls by aggregating land-use values by TAZ. Redistributes the additional households by household size based on the distribution of households by household size as observed in control data. Similarly redistributes additional household based on distribution of households by householder age, number of household workers, and household income respectively as observed in the control data.
**Updates County level population controls by adding additional person based on average household size calculated for each county from existing controls. The additional population is distributed across gender and age group based on distribution of population observed in controls for respective categories.

<h4>Table: Variables to update by user when updating update_control.S script </h4>

{| class="wikitable"
|-
! VARIABLE NAME
! DESCRIPTION
! EXAMPLE
|-
| LANDUSE_PATH
| Full path to the folder that contains the land-use file.
| 'E:\NERPMABv2.0\User.prg\DaySim_Data_Tools'
|-
| LANDUSE_FILE
| Name of the land-use file
| 'nftpo_microzones_2045.csv'
|-
| POPSIM_PATH
| Full path to the PopulationSim folder
| 'E:\NERPMABv2.0\User.prg\Population_Synthesis'
|-
| CONTROL_FOLDER
| Name of the folder containing the control data
| 'data_2045'
|-
| OVERWRITE_INITIAL
| A value of 0 will not overwrite the initial files and a value of 1 will overwrite the initial files.
| 0: Does not overwrite the control_totals_{zone}_initial.csv file<br />1: Overwrites the control_totals_{zone}_initial.csv file
|}

*Run “update_controls.bat” file, located in “./User.prg/Population_Synthesis/” folder, by double-clicking. This will call the Cube Voyager and run the “update_controls.S” script.
*The control files are now updated and are ready to be used by PopulationSim.

<h2> Development of 2030 land-use data </h2>

The 2030 land-use data was created by applying a methodology, detailed in the model documentation, to the land-use data for the base year (2015) and the BEBR forecasts. The table below shows the summary of comparisons between land-use data for the base year (2015) and the future year (2030).

<h4> Table: Comparison of 2015 and 2030 totals </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! Households<br />2015
! Households<br />2030
! Employment<br />2015
! Employment<br />2030
! Enrollment<br />2015
! Enrollment<br />2030
|-
| NASSAU
| 32,983
| 41,948
| 28,480
| 45,394
| 13,368
| 25,998
|-
| DUVAL
| 357,463
| 453,184
| 496,394
| 622,922
| 211,091
| 311,765
|-
| ST JOHNS
| 84,540
| 135,295
| 66,355
| 124,742
| 43,977
| 79,721
|-
| CLAY
| 70,523
| 100,980
| 46,539
| 81,119
| 40,877
| 75,838
|-
| BAKER
| 8,815
| 12,212
| 8,909
| 14,615
| 5,128
| 7,602
|-
| PUTNAM
| 31,278
| 35,535
| 21,521
| 32,993
| 14,856
| 22,076
|- style="font-weight:bold;"
| TOTAL
| 585,602
| 779,154
| 668,198
| 921,785
| 329,297
| 523,000
|}

<h2> Development of 2030 control data </h2>

Controls at three levels of geography are required to run PopulationSim for North Florida TPO: Super County, TAZ and MAZ. The Super COUNTY level controls were obtained from BEBR data and the TAZ and MAZ level controls were prepared using the 2030 land-use data. Please refer to the model documentation for the details of this development process.

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev2030.jpg|778x972px]]

<h2> Updating 2030 control data </h2>

When updating land-use data it is necessary to update the PopulationSim controls. The following example shows how to update the controls when adding 100 households to MAZ 1439:

<h4> Land-use file before update </h4>
[[File:lubefore45.jpg]]

*Update the land-use “nftpo_microzones_2030.csv” file by adding 100 households to MAZ 1439. The figure above shows the land-use file before the addition of 100 households and the figure below shows the land use file after the addition of 100 households.

<h4> Land-use file after update </h4>
[[File:luafter45.jpg]]

*Edit the variables listed in the table below within the CUBE script “update_controls.S” located in “./User.prg/Population_Synthesis/” folder. The script performs following actions:
**Makes a copy of control files.
**Updates MAZ level household controls by comparing existing control values with land-use values.
**Updates TAZ level household controls by aggregating land-use values by TAZ. Redistributes the additional households by household size based on the distribution of households by household size as observed in control data. Similarly redistributes additional household based on distribution of households by householder age, number of household workers, and household income respectively as observed in the control data.
**Updates County level population controls by adding additional person based on average household size calculated for each county from existing controls. The additional population is distributed across gender and age group based on distribution of population observed in controls for respective categories.

<h4>Table: Variables to update by user when updating update_control.S script </h4>
{| class="wikitable"
|-
! VARIABLE NAME
! DESCRIPTION
! EXAMPLE
|-
| LANDUSE_PATH
| Full path to the folder that contains the land-use file.
| 'E:\NERPMABv2.0\User.prg\DaySim_Data_Tools'
|-
| LANDUSE_FILE
| Name of the land-use file
| 'nftpo_microzones_2030.csv'
|-
| POPSIM_PATH
| Full path to the PopulationSim folder
| 'E:\NERPMABv2.0\User.prg\Population_Synthesis'
|-
| CONTROL_FOLDER
| Name of the folder containing the control data
| 'data_2030'
|-
| OVERWRITE_INITIAL
| A value of 0 will not overwrite the initial files and a value of 1 will overwrite the initial files.
| 0: Does not overwrite the control_totals_{zone}_initial.csv file<br />1: Overwrites the control_totals_{zone}_initial.csv file
|}

*Run “update_controls.bat” file, located in “./User.prg/Population_Synthesis/” folder, by double-clicking. This will call the Cube Voyager and run the “update_controls.S” script.

*The control files are now updated and are ready to be used by PopulationSim.

<h2> DaySim ABM Model Update </h2>

After completion of base year 2015 data aggregation from parcels to microzones, DaySim was tested for the new geographies and the results compared against the parcel results to check for consistencies. The necessary input files were created for DaySim run as explained in detail in Appendix B of the model documentation. After a successful run of DaySim using updated 2015 Microzone and Population synthesis dataset, the results were compared with previous DaySim run using parcels. The small set of tables and figures displayed here show the consistency of the 2015 dataset in DaySim performance. A complete series of tables and figures are shown in the model documentation.

<h4> Table: Estimated Households by Number of Vehicles and County (Parcel) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 309
| 2,750
| 3,899
| 1,285
| 557
| 8,800
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 2%
|-
| Clay
| 2,154
| 19,501
| 32,064
| 11,436
| 4,123
| 69,278
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 12%
|-
| Duval
| 27,194
| 126,487
| 137,367
| 42,231
| 15,633
| 348,912
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 61%
|-
| Nassau
| 1,212
| 10,006
| 14,514
| 4,681
| 1,820
| 32,233
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 6%
|-
| Putnam
| 1,594
| 11,944
| 12,800
| 3,767
| 1,543
| 31,648
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 6%
|-
| St. Johns
| 3,097
| 25,256
| 36,209
| 11,566
| 4,102
| 80,230
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 14%
|- style="font-weight:bold;"
| Total
| 35,560
| 195,944
| 236,853
| 74,966
| 27,778
| 571,101
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 100%
|}

<h4> Table: Estimated Households by Number of Vehicles and County (Microzone + Node to Node Distances) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 333
| 2,781
| 3,868
| 1,261
| 557
| 8,800
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 2%
|-
| Clay
| 2,491
| 19,945
| 31,772
| 11,122
| 3,948
| 69,278
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 12%
|-
| Duval
| 28,867
| 126,963
| 136,315
| 41,659
| 15,108
| 348,912
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 61%
|-
| Nassau
| 1,329
| 10,195
| 14,426
| 4,515
| 1,768
| 32,233
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 6%
|-
| Putnam
| 1,627
| 11,994
| 12,767
| 3,750
| 1,510
| 31,648
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 6%
|-
| St. Johns
| 3,795
| 25,835
| 35,721
| 11,105
| 3,774
| 80,230
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 14%
|- style="font-weight:bold;"
| Total
| 38,442
| 197,713
| 234,869
| 73,412
| 26,665
| 571,101
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 100%
|}

<h4> Table: Estimated Households by Number of Vehicles and County (Microzone + N2N Distances + PopSim 2015) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 403
| 3,021
| 3,704
| 1,015
| 672
| 8,815
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 2%
|-
| Clay
| 2,742
| 21,083
| 31,758
| 11,032
| 3,908
| 70,523
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 12%
|-
| Duval
| 29,793
| 131,763
| 131,610
| 44,856
| 16,038
| 354,060
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 61%
|-
| Nassau
| 1,471
| 12,051
| 13,617
| 3,469
| 2,375
| 32,983
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 6%
|-
| Putnam
| 2,158
| 13,690
| 10,852
| 3,059
| 1,519
| 31,278
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 5%
|-
| St. Johns
| 3,652
| 28,418
| 37,278
| 11,588
| 3,604
| 84,540
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 15%
|- style="font-weight:bold;"
| Total
| 40,219
| 210,026
| 228,819
| 75,019
| 28,116
| 582,199
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 100%
|}

<h4>Work Location Choice: HOME-WORK DISTANCES </h4>

[[File:WLCHWD.JPG|800x331px]]

<h4>School Location Choice: HOME-SCHOOL DISTANCES </h4>

[[File:SLCHSD.JPG|800x331px]]

<h2>Development of External Model Inputs </h2>

<h3>Data Sources </h3>

The source data for the X-X trip table has been obtained from Bluetooth detectors that were installed at 17 locations adjacent to highways at external model stations. This work was conducted under a separate contract with Florida DOT. The raw data from the Bluetooth deployment were obtained by HNTB and processed for further analysis by RSG.

*2010 North Florida TPO model X-X trip table (EETRIPS.dbf): (29 nodes)
The total trips originating for each external node and destined for other external nodes are calculated by assigning a proportion of the total incoming traffic (e.g. 16%). Incoming traffic is distributed from each origin external node to each other external node in proportion to the destination node’s AADT.

*EE_O-D_Study spreadsheet provided by HNTB: (17 nodes)
This spreadsheet was provided by HNTB and contains the raw matched BT X-X data, total traffic counts, total truck counts, matched BT X-X/X-I data (17 external and 150 internal nodes), and related pivot tables.

<h3>Estimating the BT Expansion Factor </h3>

The BT expansion factor (or penetration rate) is the percentage of total traffic recorded with having a discoverable Bluetooth device on or within the vehicle. The expansion factors imputed from the HNTB data were abnormally high given RSG’s prior experience in working with data of this type. RSG requested, but never received from HNTB, the raw Bluetooth data. For this reason, RSG developed a method to estimate an expansion factor for each station. The details of the method can be found in the model documentation.

<h3>Estimating External-External Trips </h3>

The raw matched X-X BT data alongside with expansion factors are used to estimate the X-X trips. The data is cleaned based on methodology described in the model documentation.

<h2>Network Updates </h2>

<h3>Network Link Updates </h3>

The previous Existing + Committed (E+C) network, known as 2040A in the 2010 model setup, was used as the starting point for the 2015 network. Table below lists the previous E+C network links that were modified to be consistent with 2015 base year conditions. Modifications included revisions to the number of lanes, facility type, and turn restrictions.

UPDATE (01/30/2020): BUSTFAC_45C was set to 0 in the 2045 network fields. This was causing problems with transit assignment resulting in very low transit ridership. RSG set this to 1 for all links since BUSTFAC_15A has it set to 1 for all links. This resolved the low transit ridership issue in the scenario year 2045.

Before setting up a model run, please make sure that in the transit route input file (or any subsequent transit route input files), the skyway modes are coded as MODE equal 24 instead of 23 and commuter rail “CR_SE-Rail” is coded as MODE equal 25 instead of 27 to be consistent with the new in-vehicle time skims.

<h4>E+C Network (2040A) Links to Change for 2015 Network</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! County
! Route
! From
! To
|-
| Nassau
| Chester Rd.
| SR A1A
| Green Pine Rd.
|-
| Duval
| First Coast Expwy. (SR 23)
| Clay CL
| Argyle Forest Blvd.
|-
| Duval
| First Coast Expwy. (SR 23)
| Argyle Forest Blvd
| I-10
|-
| Clay
| First Coast Expwy. (SR 23)
| Blanding Blvd (SR 21)
| Duval CL
|-
| Duval
| I-10 @ I-95
| Roosevelt Ave. (US 17)
| San Marco Ave.
|-
| Duval
| I-295
| SR 9B
| J.T. Butler Blvd. (SR 202)
|-
| Duval
| I-295
| I-10
| Commonwealth Ave.
|-
| Duval
| I-295
| Buckman Bridge
| I-95
|-
| Duval
| I-295
| I-95 (South)
| SR 9B
|-
| Duval
| Martin Luther King Jr. Pkwy
| at 21st St./Talleyrand Ave.
|
|-
| Duval
| SR 9B
| Philips Hwy. (US1)
| I-295
|-
| Duval
| SR 9B
| I-95
| Philips Hwy. (US1)
|-
| Duval
| US 301 (SR 200)
| South of Baldwin
| North of Baldwin
|-
| Duval
| Blanding Blvd/SR 21
| South of Old Jennings
| North of CR 218
|-
| Duval
| I-10 @ Beaver Street
| Remove access to Beaver Street
|
|-
| Nassau
| US 301 (SR 200)
| Duval CL
| Callahan
|}

<h3> Volume Count Updates </h3>

The North Florida TPO provided AADT counts from 2015 FDOT portable traffic monitoring sites at point locations throughout the network. Each count was for total vehicles and did not include directionality or time of day factors. It was assumed that counts on bidirectional roads were equally split between both directions.

These points were spatially joined to the network links and then spot checked to ensure the join was accurate. Particular attention was paid to on and off ramps as they tended to have the least accuracy.

The NERPM-AB has four time periods: AM, midday (MD), PM, and night (NT). The 2010 network contains count data for each of these periods on some links. To create time of day count data for the 2015 counts, the 2015 count was distributed between the four time periods using the same ratio as the 2010 counts. Links with a 2015 count but no 2010 time of day counts used nearby 2010 time of day counts. If no 2010 count was nearby, that 2015 count was not used.

<h2>CUBE Model Update </h2>

Several updates were made to the NERPM-AB Cube model during this project. The previous version of the model had model steps and scripting logic that was left over from the previous four-step model. Therefore, enhancements were made wherever necessary to make sure that the model system and results appear reasonable.

<h3>External-Internal Trips Calibration</h3>

While looking at auxiliary model demand it was found that IE trips are very long – average VMT per trip is approximately 32 miles, compared to 8 miles for internal trips. The VMT needed to be reduced by factoring either or both total IE or truck trips, or by factoring the length of IE trips for which the gravity model used to distribute them needed adjustment. To help inform this decision, the relationship between the observed counts and the estimated link volumes at external stations was investigated. It was found that both ends of I-95 and on I-10 were low, IE trips needed to be increased. It was also possible that the IE trips were traveling too far, which could be adjusted by making the friction factor curve used in trip distribution for IE trips steeper. A series of steps were taken to resolve this issue as discussed in the model documentation.

<h3>Transit Skimming Updates</h3>

From the initial transit assignment results, it was found that the skyway/monorail boardings were significantly lower than observed boardings. Therefore, the next model enhancement made was to add alternative-specific constants (ASC) to DaySim for transit modes based on an in-vehicle time skim. For the transit alternatives that need to be modeled in the scenarios, the in-vehicle time (IVT) needs to be skimmed in a separate skim matrix, and any tour or trip that has the transit mode in the OD Pair should get the constant. The updates are discussed in detail in the model documentation.

<h3>Highway Skimming for Tolls</h3>

In the E+C 2045 networks, express lanes or managed lanes were introduced in the highway network. Initially the toll variable, CARTOLL, was set at zero for all links. This was changed to 20 cents per mile (0.20). This value (CARTOLL) was then multiplied with the link length throughout the model setup to calculate the cost. Moreover, the occupancy for HOV2 and HOV3+ were also changed to 1.3 and 1.5, respectively.

<h2>DaySim Model Calibration and Validation</h2>

The tour destination and other sub models were calibrated using the North Florida Household Travel Survey (2017), the transit tours and trips were calibrated using the Transit On-Board Survey (2016) obtained from Transit On-Board Survey Program, and the worker flow were calibrated using CTPP (2010) obtained from Census.

The following set of tables and figures show calibration summaries:

<h4>Table: Population by Person Type</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! Person type
! Survey
! PopSim
|-
| Full Time Worker
| 540,570
| 566,852
|-
| Part Time Worker
| 96,726
| 59,179
|-
| Retired
| 172,447
| 142,992
|-
| Non-Worker
| 203,794
| 208,490
|-
| University Student
| 43,943
| 55,998
|-
| Student Age 16+
| 37,901
| 48,543
|-
| Student Age 5-15
| 185,288
| 197,734
|-
| Kid under 5
| 83,362
| 102,243
|- style="font-weight:bold;"
| Total
| 1,326,484
| 1,419,578
|}

<h4>Table: Average Home to Work Distance</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! Worker Type
! Survey
! DaySim
|-
| Full Time
| 13.7
| 13.3
|-
| Part Time
| 9.1
| 6.8
|-
| Other
| 9.9
| 14.5
|- style="font-weight:bold;"
| Total
| 13.2
| 12.4
|}

<h4>Table: Workers by County</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! CTPP
! HTS
! DaySim
|-
| 1
| 7,283
| 5,383
| 9,681
|-
| 2
| 59,391
| 80,418
| 84,565
|-
| 3
| 286,647
| 400,012
| 420,057
|-
| 4
| 18,556
| 17,424
| 32,801
|-
| 5
| 13,779
| 10,901
| 20,362
|-
| 6
| 64,325
| 79,338
| 97,402
|- style="font-weight:bold;"
| TOTAL
| 449,980
| 593,476
| 664,868
|}

<h2> Model Calibration and Validation </h2>

The 2017 north florida travel survey and the 2016 onboard rider demographic survey were used to calibrate the model. Traffic counts and transit boarding counts were used to validate the model outputs. The following tables and figures show the results of the calibrated model. These include trip length frequency distributions, tour and trip mode choice model results, highway and transit validation etc.

<h4> Table: Population and Employment </h4>

{| class="wikitable"
|-
! County
! 2015 Population
! 2015 Employment
! BEA 2015
! 2030 Population
! 2030 Employment
! 2045 Population
! 2045 Employment
|-
| Baker
| 23,138
| 9,649
| 9,695
| 32,340
| 15,876
| 37,723
| 20,123
|-
| Clay
| 189,600
| 68,871
| 69,317
| 263,882
| 120,166
| 321,984
| 156,073
|-
| Duval
| 854,757
| 624,952
| 623,596
| 1,078,136
| 786,052
| 1,231,564
| 888,333
|-
| Nassau
| 76,672
| 30,948
| 31,086
| 97,500
| 49,367
| 116,024
| 60,801
|-
| Putnam
| 71,687
| 22,605
| 22,780
| 78,328
| 34,695
| 84,790
| 38,102
|-
| St Johns
| 202,375
| 105,077
| 104,983
| 318,041
| 197,421
| 412,811
| 287,415
|-
| Grand Total
| 1,418,229
| 862,102
| 861,457
| 1,868,227
| 1,203,577
| 2,204,896
| 1,450,847
|}

<h4>Trip Length Frequency Distribution </h4>
[[File:Wsloc.JPG]]

<h4>Tour Mode Choice </h4>
[[File:Tourmode.JPG]]

<h4>Trip Mode Choice </h4>
[[File:Tripmode.JPG]]

<h4>Highway Validation </h4>
[[File:Hwyvalidation.JPG]]

<h4>Transit Validation </h4>
[[File:Trnvalidation.JPG]]

<h2>Software Requirements</h2>

PopulationSim requires the following software, already included in the model setup, and can be found in the following location w.r.t the project folder:

*Anaconda2 with Python 2.7 – “.\User.prg\Population_Synthesis\Anaconda2”

<h2>Setup and Run PopulationSim</h2>

<h3>Setup Directory</h3>

Figure below presents the directory structure for the PopulationSim setup. To set up a PopulationSim run, the user must create the directory structure as shown in below.

<h4>PopulationSim Directory Structure</h4>
[[File:PopSimDir.JPG]]

The folders and files in the directory are explained as follows:

*The data directory holds all the input data like seeds data, control data and crosswalks.
**SCOUNTY Control totals
**TAZ Control totals
**MAZ Control totals
**Geographic Crosswalk
**Household Seed table
**Person Seed table

*The Anaconda2 directory houses the core PopulationSim software files and associated libraries and packages.

*The configs folder contains the settings.yaml file and controls.csv file
**PopulationSim is configured using the settings.yaml file. For this project, it is configured to run in base mode which means it is run from beginning to end and produces a new synthetic population
**controls.csv file specifies all the targets, geography, seed table, control field and their expression to the seed table required for the PopulationSim run.

*The output folder will have the final synthetic household and person file and summary attributes after a successful run.

*This batch file activates the PopulationSim environment and then calls the run_populationsim.py Python script to launch a PopulationSim run.

*PopulationSim is run using the RunPopulationSim.bat batch file in Command Prompt Window. Before starting the run, the path to the Anaconda install must be updated within this file.

The PopulationSim procedure is automated and once you have all the data in place, the user just needs to run the batch file from the command prompt window. To run the PopulationSim for 2030 or 2045 the user need to update the ‘data_dir’ field in the settings.yaml file. The ‘data_dir’ field for different scenario years are:

*2015: The ‘data_dir’ in settings.yaml file should be set to ‘data’

*2030: The ‘data_dir’ in settings.yaml file should be set to ‘data_2030’

*2045: The ‘data_dir’ in settings.yaml file should be set to ‘data_2045’

<h2>Preparing DaySim Inputs</h2>

<h3>Transit Files</h3>

The transit stops file is created using the input transit network from Jacksonville Transit Authority: TROUTE_{Year}{Alt}.LIN
Follow the sequential step below:

*Create a geodatabase (gdb) in Cube

*Import the input transit network file and the highway network file

*Export the nodes (PTNetwork_PTNode) to a shapefile

*Open the shapefile in ArcMap and select the nodes that have STOPNODE=1. These are the transit stops

*Export the selected nodes (stops) to a new stops shapefile. Also Export the transit line file to a shapefile.

*Open the attribute table of the new stops file and add four fields: xcoord_p (double), ycoord_p (double), mode (int) and id (long).

*Calculate xcoord_p and ycoord_p using calculate geometry for the two fields.

*Join transit line by object id and calculate a new field “mode” equal to the MODE field in the line file (1-localbus, 2-skyway). Jacksonville mode codes and daysim mode codes are different, convert these to daysim mode codes before running buffer tools.

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! 2015
! 2030
! 2045
! DaySim
|-
| 21 = LB
| 21 = LB
| 21 = LB
| 1 = LB
|-
| 24 = Skyway (coded as LB)
| 23 = Skyway (coded as LB)
| 23 = Skyway (coded as LB)
| 2 = EB
|-
|
| 27 = CR
| 27 = CR
| 3 = CR
|}

*Calculate stopid as “FID+1”.

*Keep only four fields in the attribute table: id, mode, x_coord, y_coord.

*Export all records in the attribute table to a csv file: nftpo_transitstops.csv

<h2>DaySim Data Editing</h2>

With the base year moving from 2010 to 2015, microzones replacing parcels and new population data, the inputs for DaySim needed to be updated. The following steps describe the DaySim input data preparation and update for the NERPM-AB model including the tool and input data used and output formed.

<h3>Network Data Preparation</h3>

This step calculates “nearby” node pairs of microzones for shortest distance path calculations to be used in DaySim.

Tool: '''Network_DataPrepv2.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*input_node.csv (Node x,ys from an all-streets network)
*jax_MAZs_2015.dat (The coordinates of the newly developed microzones)
*jax_netprep.ctl (Network prep control file)

Output:
*input_od_pairs.csv (for input to shortest path update tool)

<h3>Shortest Path Update</h3>

This process is required to generate more accurate short distances based on an all streets network. DTALite, a dynamic traffic assignment software, is used to generate node-to-node shortest path distances using the all streets network.

Tool: '''DTALite64.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*input_od_pairs.csv (from the Network Data Preparation tool)
*input_node.csv (from all-street network)
*input_link_type.csv (from all-street network)
*input_link.csv (from all-street network)
*DTASettings.ini (settings file)

Output:
*output_shortest_path.txt (for input to Buffering microzones)

<h3>Buffering micro-zones</h3>

The Daysim buffering tool is run to prepare MAZ input file for DaySim. This step calculates the new Microzone buffer measures to be used in DaySim.

Tool: '''DSBuffTool.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*Jax_microzones_2015.csv (Base Microzone file)
*Jacksonville_Intersections.csv (Street intersections file)
*Jacksonville_transitstops.csv (Transit stops file)
*Jacksonville_openspaces.csv (Open spaces/parks file)
*input_node.csv (All-street Network nodes file)
*output_shortest_path.txt (Node-to-node shortest path distance file)

Output:
*buffered_microzone_2015.dat (to be used in DaySim)
*microzonenode.dat (to be used in DaySim)
*output_shortest_path.txt.bin (Change extension using batch file)
*output_shortest_path.txt.index (Change extension using batch file)

The file extension of the last two outputs above need to be changed for reading into DaySim. The last three output files are used in DaySim to estimate short-distances for car, walk and bike trips.

All three DaySim Data tools can be run using RunAll.bat from the command prompt window.

<h3>Running DSBuffTool.exe</h3>

Double-clicking the executable will bring up a GUI as shown in the following figure. The use of this tool is straightforward. The user just needs to specify all the inputs and click on the “Run” button at the bottom of the GUI.

<h4>DaySim Buffering Tool GUI</h4>
[[File:DBTG.JPG]]

In the “INPUT” section of the GUI, distance calculation and buffer type are set to “Euclidean” and “Logistic decay” respectively. These are the recommended settings for running this tool. If node-to-node distances obtained from an all streets network are available, distance calculation may be set to “Node-to-Node”. The default recommended parameters for logistic decay weights (described in the model design chapter) such as buffer decay slope, offset etc., are also automatically populated in the appropriate fields in the GUI.

All other inputs are to specify file paths that can be done by clicking on the “Browse” button. This pops a file dialog as show in the figure below. The user may also enter full file paths manually using keyboard.

<h4>Buffer Tool File Selection Dialog</h4>
[[File:BTFSD.JPG]]

All file specification fields for the tool are described below. Details about formats for the input files can be found in the directory and data structures chapter.

*MAZ Data File: This is the base MAZ file. It is obtained by running the disaggregation tool.

*Intersection Data File: This file has the coordinates of intersections and the number of links intersecting at each one of them.

*Transit Stops File: This file has the transit stop location coordinates by transit sub-mode.

*Open Spaces File: This file has the locations and area of parks/open spaces in the model region.

*Circuity Data File: This is only required for “Circuity” distance calculation that is now an obsolete method.

When “Node-to-Node” distance calculation is selected, the user need to select a format of the input node-node distance file: output of DTALite or input file used in DaySim. The following files are only needed for “Node-to-Node” distance calculation method.

*Node Data File: This file contains the coordinates of node from an all streets network.

*Node to Node Distance File: This file contains the network shortest path distances between a list of node pairs that are within 3 miles of each other. It is either an output from DTALite or a distance file input to DaySim.

*MAZ-Node Correspondence File: This is an intermediate file created during the buffering process and contains the correspondence between MAZs and nodes.

*Open spaces-Node Correspondence File: This is an intermediate file created during the buffering process and contains the correspondence between open spaces and nodes.

Specification of the following XML files is optional. However, they store the input configuration of a buffering tool run and make it convenient to use the tool.

*Output XML File: If a file is specified in this field, an XML file containing all the input information specified by the user is stored upon clicking on “Run” button that initiated the buffer process.

*XML Input File: Once an XML file has been stored as described above, it can be selected to automatically populate all the input fields required for a buffer run. This can be used to re-run a buffer process with the same inputs or modify a few inputs to generate a different buffered MAZ file.

Finally, Buffered Output File field is used to specify the location and name of the buffered microzone output file that needs to go into DaySim inputs folder.

<h3>DaySim Files Update</h3>

The following files were updated in the latest 2015 DaySim run:

*New Microzone file containing aggregated household and employment inputs and buffered measures instead of parcels

*Household file containing all household information with respect to the microzones

*DaySim Configuration file containing the DaySim execution settings

*A set of updated DaySim input files

*A set of new Data files for short distance calculation

To accurately calculate short car, walk and bike trips, short distance node-to-node measure is introduced in DaySim. This includes the following changes:

*Change in DaySim Configuration file

*Utilization of three additional files in DaySim:
**Microzone node file (microzonenode.dat)
**Node Index file (output_shortest_path_txt_index.dat)
**Node distance file (output_shortest_path_txt_bin.dat)

<h3>Activating TAZ</h3>

The model contains a list of TAZs that are dummy TAZs that act as a placeholder to be later used when building new TAZs. The steps to utilize or activate these dummy zones is as follow:

*Identify the dummy TAZ that needs to be activated.

*Update the input network MicroCodedHnet42.net within Cube.
**Relocate the dummy TAZ to the centroid of new TAZ.
**Update the attributes of the dummy TAZ.
**Build network links that would connect the dummy TAZ to appropriate nodes on the network.
**Update the attributes of these network links.

*Identify the overlap/association between existing MAZs and the new TAZ.

*Update the land-use file nftpo_microzones_year.csv.
**Update the “taz_p” field for MAZs that are now identified as associated with new TAZ for all scenario years.

*Update the PopulationSim Inputs.
**Update the fields associated with MAZs identified in the earlier step with updated TAZ in geo_cross_walk.csv.
**Update the TAZ field for identified MAZs in control_totals_maz.csv file.
**Update the control_totals_taz.csv file by adding a row for dummy TAZ and recording appropriate values in rest of the fields. It should be ensured that the values entered are reasonable for e.g. household size values should not all be 0 and should match up to total households.
**Go through the steps described in Updating Land-use Manually later in this document.

*Update scenario input files in “scenario/Input/” folder.
**Add/Update the “Zone_id” and “Zone_ordinal” field in the _jax_taz_indexes.dat file located in “scenario/Input/DaySimInput/01_TAZ_Index/” folder to the dummy TAZ. Set the “Dest_eligible” field for this TAZ to a value of 1.
**Add/Update the TAZ field in the emp_year.dbf file located in “scenario/Input/DaySimInput/02_Parcel/” folder.
**Add/Update the TAZ field in the _jax_worker_ixxifractions.dat file located in “scenario/Input/DaySimInput/05_ixxi/” folder.
**Add/Update the TAZ field in the county_districts.csv file located in the “scenario/Input/DaySimInput/08_Summaries/” folder.
**Add/Update the TAZ field in the RIVERCROSS.csv file located in the “scenario/Input/” folder.

This concludes the steps needed to activate a dummy TAZ, and the use can now run the regular model.

<h3>Updating Land-use Manually</h3>

When updating land-use data, like household or population attributes, for the base or future year, the steps below should be followed to ensure proper propagation of changes throughout the model:

*The starting point for such changes is the buffering micro-zones step. One of the inputs to the buffering tool is the MAZ file, for example, nftpo_microzones_2015.csv for the 2015 scenario. The user should manually update the land-use file for changes required in specific zones because of new development, changed land-use, etc. The user should then run the buffer tool and copy the output file (buffered_maz_2015.dat) to the scenario input directory.
*The next step is to update the PopulationSim controls by following the guidelines in Section 3.1 for the base year or in Appendix A (Updating Control Data). The user should then run PopulationSim.

*The user should then run the “popsim_to_daysim.bat” file by double clicking it in Windows or running it from a DOS command promt. The batch file runs the “convert.py” script that converts PopulationSim output to DaySim inputs. The DaySim inputs are output in the “./User.prg/PopulationSim/popsim_to_daysim/output/” folder. The output should be renamed as shown in Table 78. These outputs should be copied to the model scenario input directory.

<h4>Table: Rename popsim_to_daysim.bat output</h4>

{| class="wikitable" style="font-weight:bold; text-align:center; vertical-align:middle;"
|-
! rowspan="2" | Output File
! colspan="3" | Rename File
|-
| Base2015
| INT2030
| CF2045
|- style="font-weight:normal; text-align:left;"
| household_2015.dat
| household_2015.dat
| household_2030.dat
| household_2045.dat
|- style="font-weight:normal; text-align:left;"
| person_2015.dat
| person_2015.dat
| person_2030.dat
| person_2045.dat
|}

This concludes the land-use updates step, and the user can now run the regular model.

Support/Training

2020-12-22T14:45:14Z

Bstabler: Updated time stamps.

For support, please first check "[[Frequently Asked Questions]]."

*[[Media:June2020_Training_NERPM-AB_2.0.pdf|NERPMAB2_Update.pdf (DEC 2020)]]
*[https://vimeo.com/452635204 NERPMAB2 Webinar Video Recording (AUG 2020)]

*[[Media:NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf|NERPMAB1v3_Update_Overview_Amended.pdf (SEP 2017)]]
*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording (FEB 2017)]

*[[Media:NERPMAB1v2_Training_June_2016.pdf|NERPMABv2 Training Slides (JUN 2016)]]
*[https://youtu.be/IApPyy-Zi_s NERPMAB1v2 Webinar (JUN 2016)]
*[[Media:NERPMAB1v2 Webinar Excercise Revised (2).pdf|NERPMABv2 Exercise (JUN 2016)]]

If you need additional information, please contact [mailto:mlocklear@northfloridatpo.com?cc=Stephen.Lawe@rsginc.com;Ameera.Sayeed@dot.state.fl.us;Hadi.Sadrsadat@rsginc.com Milton Locklear]

Thank you!

North Florida TPO Model Users’ Guide

2020-12-22T14:29:45Z

Bstabler: Rewording.

<h2>Northeast Regional Planning Model: Activity Based</h2>

[[File:model_image.jpg]]

<h5>PREPARED FOR: North Florida Transportation Planning Organization</h5>

Latest Release: [https://www.fsutmsonline.net/index.php?/model_pages/modD22/index/ NERPMABv2.1]


*[[Chapter: 1.0 Overview]]
*[[Chapter: 2.0 Hardware & Software Requirements]]
*[[Chapter: 3.0 Model Design]]
*[[Chapter: 4.0 Directory & Data Structures]]
*[[Chapter: 5.0 User Interface & Running the Model]]
*[[Chapter: 6.0 Configuring a Scenario]]
*[[Chapter: 7.0 Version Updates]]
*[[Frequently Asked Questions]]
*[[Support/Training]]

North Florida TPO Model Users’ Guide

2020-12-22T14:29:00Z

Bstabler: Added link to model setup.

<h2>Northeast Regional Planning Model: Activity Based</h2>

[[File:model_image.jpg]]

<h5>PREPARED FOR: North Florida Transportation Planning Organization</h5>

Current Release: [https://www.fsutmsonline.net/index.php?/model_pages/modD22/index/ NERPMABv2.1]


*[[Chapter: 1.0 Overview]]
*[[Chapter: 2.0 Hardware & Software Requirements]]
*[[Chapter: 3.0 Model Design]]
*[[Chapter: 4.0 Directory & Data Structures]]
*[[Chapter: 5.0 User Interface & Running the Model]]
*[[Chapter: 6.0 Configuring a Scenario]]
*[[Chapter: 7.0 Version Updates]]
*[[Frequently Asked Questions]]
*[[Support/Training]]

File:June2020 Training NERPM-AB 2.0.pdf

2020-12-22T14:23:47Z

Bstabler: Bstabler uploaded a new version of File:June2020 Training NERPM-AB 2.0.pdf

Support/Training

2020-12-22T14:20:52Z

Bstabler: Updated the dates

For support, please first check "[[Frequently Asked Questions]]."

*[[Media:June2020_Training_NERPM-AB_2.0.pdf|NERPMAB2_Update.pdf (DEC 2020)]]
*[https://vimeo.com/452635204 NERPMAB2 Webinar Video Recording (AUG 2020)]

*[[Media:NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf|NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf]]
*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording]

*[[Media:NERPMAB1v2_Training_June_2016.pdf|NERPMABv2 Training Slides]]
*[https://youtu.be/IApPyy-Zi_s NERPMAB1v2 Webinar, June 15, 2016]
*[[Media:NERPMAB1v2 Webinar Excercise Revised (2).pdf|NERPMABv2 Exercise]]

If you need additional information, please contact [mailto:mlocklear@northfloridatpo.com?cc=Stephen.Lawe@rsginc.com;Ameera.Sayeed@dot.state.fl.us;Hadi.Sadrsadat@rsginc.com Milton Locklear]

Thank you!

7.3 Version NERPMAB2

2020-12-08T02:45:35Z

Bstabler: Changed some formatting and updated some references.

[[Category:7.0 Version Updates]]
'''7.3 VERSION NERPM-AB2'''

This section documents the set of improvements that were made to the NERPM model for the release of NERPM-AB2. The improvements focused on:

*Base year updated from 2010 to 2015
*Re-built all land use inputs for 2015 and future
*Switched from parcels to microzones to ease data preparation
*Switched from PopGen to PopulationSim
*Updated the highway network to 2015 (NFTPO)
*Updated the transit network to 2016 (JTA)
*Updated external traffic volumes and distributions based on Bluetooth OD data (FDOT)
*Upgraded from the NFTPO specific version of DaySim to the multiple-agency (a.k.a. core) version, which is faster, more stable, and includes new features such as Transportation Network Companies (TNC) mode (i.e. Uber and Lyft)
*Updated future year scenarios

<h2> Parcel to Microzone (MAZ) </h2>

In the previous version of the model, the unit of spatial analysis was parcels. There were approximately 700,000 parcels in the region, which made it difficult to maintain, edit and attribute, both the existing and alternative scenarios. This led to the creation and use of microzones as an alternative, which is halfway between parcels and TAZs and are easier to maintain. As you can see in the table, there are around 55 thousand microzones against 700 thousand parcels. This makes it much easier for the user to update land use information such as employment, enrollment etc. The total number of TAZs in this version is 1862. The plot shows the TAZ and the MAZ layer around downtown Jacksonville. This shows how TAZs and MAZs are related. You can see in the plot that MAZs in downtown tend to be smaller than the ones in the periphery. If you have any questions about the development of MAZs, please refer to the model documentation for more details.
{| class="wikitable"
|-
! <br />County
! <br />Number of Parcels
! <br />% Freq
! <br />Number of MAZ
! <br />% Freq
! <br />Number of TAZ
! <br />% Freq
|-
| <br />Baker
| <br />12,490
| <br />1.78
| <br />1,735
| <br />3.14
| <br />29
| <br />1.56
|-
| <br />Clay
| <br />84,529
| <br />12.02
| <br />7,796
| <br />14.13
| <br />184
| <br />9.88
|-
| <br />Duval
| <br />355,805
| <br />50.59
| <br />28,263
| <br />51.22
| <br />1,281
| <br />68.80
|-
| <br />Nassau
| <br />47,443
| <br />6.75
| <br />4,455
| <br />8.07
| <br />108
| <br />5.80
|-
| <br />Putnam
| <br />102,053
| <br />14.51
| <br />6,652
| <br />12.05
| <br />44
| <br />2.36
|-
| <br />St. Johns
| <br />100,950
| <br />14.35
| <br />6,283
| <br />11.39
| <br />216
| <br />11.60
|-
| <br />Total
| <br />703,270
| <br />100.00
| <br />55,184
| <br />100.00
| <br />1,862
| <br />100.00
|}

<h4> Figure: MAZ and TAZ layer in Downtown Jacksonville </h4>

[[File:Maztaz.jpg]]

<h2> Land-use data updated to base year 2015 </h2>

Once the microzones were formed the following land-use data were updated for the base year 2015:

<h3> Household Data </h3>

The household data at parcel level was aggregated to microzone level for the base year 2015.

<h3> Employment Data </h3>

The employment data was updated using three sources of data:

*The 2010 parcel employment data projected to 2015

*Data provided by Infogroup and edited by North Florida TPO

*Data provided by Cambridge Systematics as described in their June 30, 2017 memorandum “Development of 2015 Employment Data” for FDOT

Please refer to the model documentation for the details on how these data sources were utilized.

<h3> School Enrollment Data </h3>

School enrollment data from the Florida Department of Education (FDOE) provided by North Florida TPO was used to develop school enrollment data at microzone level for the base year 2015. The details of this development process can be found in the model documentation.

<h3> Parking </h3>

The parking data was derived from Florida DOT parking shapefiles "Parking surface" and "g100freeway". The website [https://www.bestparking.com/jacksonville-fl-parking/ Jacksonville, FL Parking] was also used along with the shapefiles to develop parking data.

<h3> Lodging </h3>

A list of 367 known hotels, motels, and bed and breakfasts from the Florida Department of Business and Professional Regulation provided by North Florida TPO was used to develop lodging information.

<h3> Special Generators </h3>

Jacksonville International Airport and St. Augustine are the two special generators that are effectively acting as a special generators in the model.

The Airport had an attraction value of 15,000 in 2010. Based on several sources, RSG determined that the passenger traffic at the airport has decreased slightly between 2010 and 2015. CAPA – Centre for Aviation noted an overall downward trend in traffic since 2008 but traffic began to pick up in 2013 . The airport’s website lists the 2010 and 2015 number of passengers as 5,602,000 and 5,502,000 respectively, which represents a 1.8% reduction in traffic. FDOT’s 2015 Airport Profile of JAX showed similar findings. Given the small change over the years, we recommend leaving the airport’s attraction value at 15,000 for 2015.

St. Augustine had an attraction value of 2,288 in 2010. This number accounts for tourists coming to the area. In 2010 and 2015, St. Johns County had 3.4 million and 6.3 million visitors, respectively, which represents an 85% growth over the five-year period. The 2015 number was considered an anomaly because it was the City’s 450th birthday. However, 2016 was even higher with 6.8 million visitors, so an 85% increase may be reasonable. Increasing the attractions value by 85% to 4,240 could have a noticeable impact on links near the TAZ.

<h2> PopulationSim </h2>

*PopulationSim is an open platform for population synthesis

*Replaced PopGen in this version of NERPM-AB

*Has better demographic and geographic methods compared to PopGen

*Actively maintained by the travel modeling community

*Run only when there are major changes in the landuse data

[http://https://activitysim.github.io/populationsim/# PopulationSim Wiki]

<h3> Input Data </h3>

The main inputs to a population synthesizer are

*Disaggregate population samples (Seed Sample)

*Marginal control distributions (Control variables)

The Seed sample is obtained from the [https://www.census.gov/programs-surveys/acs/data/pums.html Census Public Use Microdata Sample (PUMS)] and the Marginal distributions of person and household attributes are obtained from [https://www.bebr.ufl.edu/ Bureau of Economic and Business Research (BEBR)] and Census respectively.

The next step is the preparation of inputs to PopulationSim which includes:

<h4> Geographic cross-walk </h4>

PopulationSim can handle any number of nested geographies. The geography level (hierarchy) selected for NERPM-AB 2015 to which the marginal distributions are specified are as follows:

*Meta Geography: Super County (SCOUNTY)

*Seed Geography: PUMA

*Sub-seed Geography: TAZ and MAZ

Super-County is chosen instead of County due to overlapping of county and PUMA boundaries. Super County is the combination of multiple County to form one geographic area.

<h4> Seed population (Household and Person tables) </h4>

One of the main requirements for the seed sample is that it should be representative of the modeling region. The seed sample is obtained from PUMS dataset. The PUMS data contain five years of household records. The seed sample should be representative of the modeling region. It must contain all the specified control variables, as well as any variables that are needed for the travel model but not specified as controls. The PUMS data is downloaded from PUMS website and it is extracted both demographically and geographically for the North Florida TPO region using PUMA codes conforming to the region. There are 11 PUMA regions in the North Florida TPO area. The seed sample must include an initial weight field. The model setup includes the Seed sample that contains a weight field, WGTP, which is used for control of total households.

<h4> Controls </h4>

Controls or targets are the marginal distributions that form the constraints for the population synthesis procedure. The objective of the population synthesis procedure is to produce a synthetic population with attributes matching these marginal distributions. The controls were developed at three geographic levels:

<h5> Super County </h5>

The raw input data was downloaded at county level from [https://www.bebr.ufl.edu/sites/default/files/Research%20Reports/projections_2016_asrh.xlsx BEBR].
Next, the data was aggregated to super county level. The data at super county level are person level attributes:

*Male

*Female

*Age 0-4

*Age 5-17

*Age 18-24

*Age 25-54

*Age 55+

<h6> Table: BEBR Super County Totals (2015) </h6>

{| class="wikitable"
|-
! SCOUNTY
! PERSONS
! MALE
! FEMALE
! AGE0-4
! AGE5-17
! AGE18-24
! AGE25-54
! AGE55+
|-
| 12109107
| 286,322
| 140,049
| 146,273
| 15,257
| 47,539
| 23,702
| 104,701
| 95,123
|-
| 12031000
| 905,574
| 440,059
| 465,515
| 61,304
| 146,443
| 87,517
| 379,741
| 230,569
|-
| 12003089
| 103,553
| 51,969
| 51,584
| 5,963
| 16,366
| 8,505
| 38,968
| 33,751
|-
| 12019000
| 201,277
| 98,400
| 102,877
| 12,543
| 37,662
| 18,453
| 79,447
| 53,172
|}

<h5> TAZ </h5>

The raw input data was downloaded at various levels [Source: 2011-15 American Community Survey five-year estimates, ACS5] and it was processed at TAZ level to generate household attributes:

*Households by Household Size at Block Group Level
**Household Size 1
**Household Size 2
**Household Size 3
**Household Size 4+

*Tenure by Age of Householder at Block Group Level
**Householder Age 15-44
**Householder Age 45-64
**Householder Age 65+

*Households by number of workers at Census Tract Level
**Zero household workers
**One household workers
**Two household workers
**Three or more than three household workers

*HHs by income at Census Tract Level
**Household income $0-$24,999
**Household income $25,000-$59,999
**Household income $60,000-$99,999
**Household income $100,000+

<h5> MAZ </h5>

The raw input data was obtained at parcel level for the year 2010 and calculated for 2015 using growth factor from 2010 to 2015. The data obtained is total number of households and then aggregated for each MAZ. Once the control files are prepared, the total number of households and persons are calculated across geographies to check for consistency as shown in Table below.

<h5> Table: Control data summary (2015) </h5>

{| class="wikitable" style="font-weight:bold; vertical-align:middle;"
|- style="text-align:center;"
! style="text-align:left;" | Data
! Geography
! Value
|-
| Total number of households
| style="text-align:center;" | MAZ
| style="font-weight:normal;" | 582,199
|-
| Total number of households
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 582,199
|-
| Number of households in HHSIZE=1
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 155,522
|-
| Number of households in HHSIZE=2
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 196,050
|-
| Number of households in HHSIZE=3
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 87,996
|-
| Number of households in HHSIZE=4+
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 110,821
|-
| Total number of persons (from HHSIZE)
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 1,397,535
|-
| Total number of persons
| style="text-align:center;" | SCOUNTY
| style="font-weight:normal;" | 1,397,534
|}

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev.jpg]][[File:Prmse.jpg]]

<h2> Development of 2045 land-use data </h2>

The 2045 land-use data was created by applying a methodology, detailed in the model documentation, to the land-use data for the base year (2015) and the BEBR forecasts. The table below shows the summary of comparisons between land-use data for the base year (2015) and the future year (2045).

<h4> Table: Comparison of 2015 and 2045 totals </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! Households<br />2015
! Households<br />2045
! Employment<br />2015
! Employment<br />2045
! Enrollment<br />2015
! Enrollment<br />2045
|-
| NASSAU
| 32,983
| 51,902
| 28,480
| 55,393
| 13,368
| 31,576
|-
| DUVAL
| 357,463
| 541,730
| 496,394
| 708,478
| 211,091
| 362,983
|-
| ST JOHNS
| 84,540
| 181,500
| 66,355
| 179,066
| 43,977
| 116,687
|-
| CLAY
| 70,523
| 132,830
| 46,539
| 105,158
| 40,877
| 98,998
|-
| BAKER
| 8,815
| 15,117
| 8,909
| 18,491
| 5,128
| 9,876
|-
| PUTNAM
| 31,278
| 40,307
| 21,521
| 35,564
| 14,856
| 23,404
|- style="font-weight:bold;"
| TOTAL
| 585,602
| 963,386
| 668,198
| 1,102,150
| 329,297
| 643,524
|}

<h2> Development of 2045 control data </h2>

Controls at three levels of geography are required to run PopulationSim for North Florida TPO: Super County, TAZ and MAZ. The Super COUNTY level controls were obtained from BEBR data and the TAZ and MAZ level controls were prepared using the 2045 land-use data. Please refer to the model documentation for the details of this development process.

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev2045.jpg|778x972px]]

<h2> Updating 2045 control data </h2>

When updating land-use data it is necessary to update the PopulationSim controls. The following example shows how to update the controls when adding 100 households to MAZ 1439:

<h4> Land-use file before update </h4>
[[File:lubefore45.jpg]]

*Update the land-use “nftpo_microzones_2045.csv” file by adding 100 households to MAZ 1439. The figure above shows the land-use file before the addition of 100 households and the figure below shows the land use file after the addition of 100 households.

<h4> Land-use file after update </h4>
[[File:luafter45.jpg]]

*Edit the variables listed in the table below within the CUBE script “update_controls.S” located in “./User.prg/Population_Synthesis/” folder. The script performs following actions:
**Makes a copy of control files.
**Updates MAZ level household controls by comparing existing control values with land-use values.
**Updates TAZ level household controls by aggregating land-use values by TAZ. Redistributes the additional households by household size based on the distribution of households by household size as observed in control data. Similarly redistributes additional household based on distribution of households by householder age, number of household workers, and household income respectively as observed in the control data.
**Updates County level population controls by adding additional person based on average household size calculated for each county from existing controls. The additional population is distributed across gender and age group based on distribution of population observed in controls for respective categories.

<h4>Table: Variables to update by user when updating update_control.S script </h4>

{| class="wikitable"
|-
! VARIABLE NAME
! DESCRIPTION
! EXAMPLE
|-
| LANDUSE_PATH
| Full path to the folder that contains the land-use file.
| 'E:\NERPMABv2.0\User.prg\DaySim_Data_Tools'
|-
| LANDUSE_FILE
| Name of the land-use file
| 'nftpo_microzones_2045.csv'
|-
| POPSIM_PATH
| Full path to the PopulationSim folder
| 'E:\NERPMABv2.0\User.prg\Population_Synthesis'
|-
| CONTROL_FOLDER
| Name of the folder containing the control data
| 'data_2045'
|-
| OVERWRITE_INITIAL
| A value of 0 will not overwrite the initial files and a value of 1 will overwrite the initial files.
| 0: Does not overwrite the control_totals_{zone}_initial.csv file<br />1: Overwrites the control_totals_{zone}_initial.csv file
|}

*Run “update_controls.bat” file, located in “./User.prg/Population_Synthesis/” folder, by double-clicking. This will call the Cube Voyager and run the “update_controls.S” script.
*The control files are now updated and are ready to be used by PopulationSim.

<h2> Development of 2030 land-use data </h2>

The 2030 land-use data was created by applying a methodology, detailed in the model documentation, to the land-use data for the base year (2015) and the BEBR forecasts. The table below shows the summary of comparisons between land-use data for the base year (2015) and the future year (2030).

<h4> Table: Comparison of 2015 and 2030 totals </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! Households<br />2015
! Households<br />2030
! Employment<br />2015
! Employment<br />2030
! Enrollment<br />2015
! Enrollment<br />2030
|-
| NASSAU
| 32,983
| 41,948
| 28,480
| 45,394
| 13,368
| 25,998
|-
| DUVAL
| 357,463
| 453,184
| 496,394
| 622,922
| 211,091
| 311,765
|-
| ST JOHNS
| 84,540
| 135,295
| 66,355
| 124,742
| 43,977
| 79,721
|-
| CLAY
| 70,523
| 100,980
| 46,539
| 81,119
| 40,877
| 75,838
|-
| BAKER
| 8,815
| 12,212
| 8,909
| 14,615
| 5,128
| 7,602
|-
| PUTNAM
| 31,278
| 35,535
| 21,521
| 32,993
| 14,856
| 22,076
|- style="font-weight:bold;"
| TOTAL
| 585,602
| 779,154
| 668,198
| 921,785
| 329,297
| 523,000
|}

<h2> Development of 2030 control data </h2>

Controls at three levels of geography are required to run PopulationSim for North Florida TPO: Super County, TAZ and MAZ. The Super COUNTY level controls were obtained from BEBR data and the TAZ and MAZ level controls were prepared using the 2030 land-use data. Please refer to the model documentation for the details of this development process.

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev2030.jpg|778x972px]]

<h2> Updating 2030 control data </h2>

When updating land-use data it is necessary to update the PopulationSim controls. The following example shows how to update the controls when adding 100 households to MAZ 1439:

<h4> Land-use file before update </h4>
[[File:lubefore45.jpg]]

*Update the land-use “nftpo_microzones_2030.csv” file by adding 100 households to MAZ 1439. The figure above shows the land-use file before the addition of 100 households and the figure below shows the land use file after the addition of 100 households.

<h4> Land-use file after update </h4>
[[File:luafter45.jpg]]

*Edit the variables listed in the table below within the CUBE script “update_controls.S” located in “./User.prg/Population_Synthesis/” folder. The script performs following actions:
**Makes a copy of control files.
**Updates MAZ level household controls by comparing existing control values with land-use values.
**Updates TAZ level household controls by aggregating land-use values by TAZ. Redistributes the additional households by household size based on the distribution of households by household size as observed in control data. Similarly redistributes additional household based on distribution of households by householder age, number of household workers, and household income respectively as observed in the control data.
**Updates County level population controls by adding additional person based on average household size calculated for each county from existing controls. The additional population is distributed across gender and age group based on distribution of population observed in controls for respective categories.

<h4>Table: Variables to update by user when updating update_control.S script </h4>
{| class="wikitable"
|-
! VARIABLE NAME
! DESCRIPTION
! EXAMPLE
|-
| LANDUSE_PATH
| Full path to the folder that contains the land-use file.
| 'E:\NERPMABv2.0\User.prg\DaySim_Data_Tools'
|-
| LANDUSE_FILE
| Name of the land-use file
| 'nftpo_microzones_2030.csv'
|-
| POPSIM_PATH
| Full path to the PopulationSim folder
| 'E:\NERPMABv2.0\User.prg\Population_Synthesis'
|-
| CONTROL_FOLDER
| Name of the folder containing the control data
| 'data_2030'
|-
| OVERWRITE_INITIAL
| A value of 0 will not overwrite the initial files and a value of 1 will overwrite the initial files.
| 0: Does not overwrite the control_totals_{zone}_initial.csv file<br />1: Overwrites the control_totals_{zone}_initial.csv file
|}

*Run “update_controls.bat” file, located in “./User.prg/Population_Synthesis/” folder, by double-clicking. This will call the Cube Voyager and run the “update_controls.S” script.

*The control files are now updated and are ready to be used by PopulationSim.

<h2> DaySim ABM Model Update </h2>

After completion of base year 2015 data aggregation from parcels to microzones, DaySim was tested for the new geographies and the results compared against the parcel results to check for consistencies. The necessary input files were created for DaySim run as explained in detail in Appendix B of the model documentation. After a successful run of DaySim using updated 2015 Microzone and Population synthesis dataset, the results were compared with previous DaySim run using parcels. The small set of tables and figures displayed here show the consistency of the 2015 dataset in DaySim performance. A complete series of tables and figures are shown in the model documentation.

<h4> Table: Estimated Households by Number of Vehicles and County (Parcel) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 309
| 2,750
| 3,899
| 1,285
| 557
| 8,800
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 2%
|-
| Clay
| 2,154
| 19,501
| 32,064
| 11,436
| 4,123
| 69,278
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 12%
|-
| Duval
| 27,194
| 126,487
| 137,367
| 42,231
| 15,633
| 348,912
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 61%
|-
| Nassau
| 1,212
| 10,006
| 14,514
| 4,681
| 1,820
| 32,233
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 6%
|-
| Putnam
| 1,594
| 11,944
| 12,800
| 3,767
| 1,543
| 31,648
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 6%
|-
| St. Johns
| 3,097
| 25,256
| 36,209
| 11,566
| 4,102
| 80,230
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 14%
|- style="font-weight:bold;"
| Total
| 35,560
| 195,944
| 236,853
| 74,966
| 27,778
| 571,101
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 100%
|}

<h4> Table: Estimated Households by Number of Vehicles and County (Microzone + Node to Node Distances) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 333
| 2,781
| 3,868
| 1,261
| 557
| 8,800
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 2%
|-
| Clay
| 2,491
| 19,945
| 31,772
| 11,122
| 3,948
| 69,278
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 12%
|-
| Duval
| 28,867
| 126,963
| 136,315
| 41,659
| 15,108
| 348,912
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 61%
|-
| Nassau
| 1,329
| 10,195
| 14,426
| 4,515
| 1,768
| 32,233
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 6%
|-
| Putnam
| 1,627
| 11,994
| 12,767
| 3,750
| 1,510
| 31,648
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 6%
|-
| St. Johns
| 3,795
| 25,835
| 35,721
| 11,105
| 3,774
| 80,230
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 14%
|- style="font-weight:bold;"
| Total
| 38,442
| 197,713
| 234,869
| 73,412
| 26,665
| 571,101
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 100%
|}

<h4> Table: Estimated Households by Number of Vehicles and County (Microzone + N2N Distances + PopSim 2015) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 403
| 3,021
| 3,704
| 1,015
| 672
| 8,815
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 2%
|-
| Clay
| 2,742
| 21,083
| 31,758
| 11,032
| 3,908
| 70,523
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 12%
|-
| Duval
| 29,793
| 131,763
| 131,610
| 44,856
| 16,038
| 354,060
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 61%
|-
| Nassau
| 1,471
| 12,051
| 13,617
| 3,469
| 2,375
| 32,983
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 6%
|-
| Putnam
| 2,158
| 13,690
| 10,852
| 3,059
| 1,519
| 31,278
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 5%
|-
| St. Johns
| 3,652
| 28,418
| 37,278
| 11,588
| 3,604
| 84,540
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 15%
|- style="font-weight:bold;"
| Total
| 40,219
| 210,026
| 228,819
| 75,019
| 28,116
| 582,199
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 100%
|}

<h4>Work Location Choice: HOME-WORK DISTANCES </h4>

[[File:WLCHWD.JPG|800x331px]]

<h4>School Location Choice: HOME-SCHOOL DISTANCES </h4>

[[File:SLCHSD.JPG|800x331px]]

<h2>Development of External Model Inputs </h2>

<h3>Data Sources </h3>

The source data for the X-X trip table has been obtained from Bluetooth detectors that were installed at 17 locations adjacent to highways at external model stations. This work was conducted under a separate contract with Florida DOT. The raw data from the Bluetooth deployment were obtained by HNTB and processed for further analysis by RSG.

*2010 North Florida TPO model X-X trip table (EETRIPS.dbf): (29 nodes)
The total trips originating for each external node and destined for other external nodes are calculated by assigning a proportion of the total incoming traffic (e.g. 16%). Incoming traffic is distributed from each origin external node to each other external node in proportion to the destination node’s AADT.

*EE_O-D_Study spreadsheet provided by HNTB: (17 nodes)
This spreadsheet was provided by HNTB and contains the raw matched BT X-X data, total traffic counts, total truck counts, matched BT X-X/X-I data (17 external and 150 internal nodes), and related pivot tables.

<h3>Estimating the BT Expansion Factor </h3>

The BT expansion factor (or penetration rate) is the percentage of total traffic recorded with having a discoverable Bluetooth device on or within the vehicle. The expansion factors imputed from the HNTB data were abnormally high given RSG’s prior experience in working with data of this type. RSG requested, but never received from HNTB, the raw Bluetooth data. For this reason, RSG developed a method to estimate an expansion factor for each station. The details of the method can be found in the model documentation.

<h3>Estimating External-External Trips </h3>

The raw matched X-X BT data alongside with expansion factors are used to estimate the X-X trips. The data is cleaned based on methodology described in the model documentation.

<h2>Network Updates </h2>

<h3>Network Link Updates </h3>

The previous Existing + Committed (E+C) network, known as 2040A in the 2010 model setup, was used as the starting point for the 2015 network. Table below lists the previous E+C network links that were modified to be consistent with 2015 base year conditions. Modifications included revisions to the number of lanes, facility type, and turn restrictions.

UPDATE (01/30/2020): BUSTFAC_45C was set to 0 in the 2045 network fields. This was causing problems with transit assignment resulting in very low transit ridership. RSG set this to 1 for all links since BUSTFAC_15A has it set to 1 for all links. This resolved the low transit ridership issue in the scenario year 2045.

Before setting up a model run, please make sure that in the transit route input file (or any subsequent transit route input files), the skyway modes are coded as MODE equal 24 instead of 23 and commuter rail “CR_SE-Rail” is coded as MODE equal 25 instead of 27 to be consistent with the new in-vehicle time skims.

<h4>E+C Network (2040A) Links to Change for 2015 Network</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! County
! Route
! From
! To
|-
| Nassau
| Chester Rd.
| SR A1A
| Green Pine Rd.
|-
| Duval
| First Coast Expwy. (SR 23)
| Clay CL
| Argyle Forest Blvd.
|-
| Duval
| First Coast Expwy. (SR 23)
| Argyle Forest Blvd
| I-10
|-
| Clay
| First Coast Expwy. (SR 23)
| Blanding Blvd (SR 21)
| Duval CL
|-
| Duval
| I-10 @ I-95
| Roosevelt Ave. (US 17)
| San Marco Ave.
|-
| Duval
| I-295
| SR 9B
| J.T. Butler Blvd. (SR 202)
|-
| Duval
| I-295
| I-10
| Commonwealth Ave.
|-
| Duval
| I-295
| Buckman Bridge
| I-95
|-
| Duval
| I-295
| I-95 (South)
| SR 9B
|-
| Duval
| Martin Luther King Jr. Pkwy
| at 21st St./Talleyrand Ave.
|
|-
| Duval
| SR 9B
| Philips Hwy. (US1)
| I-295
|-
| Duval
| SR 9B
| I-95
| Philips Hwy. (US1)
|-
| Duval
| US 301 (SR 200)
| South of Baldwin
| North of Baldwin
|-
| Duval
| Blanding Blvd/SR 21
| South of Old Jennings
| North of CR 218
|-
| Duval
| I-10 @ Beaver Street
| Remove access to Beaver Street
|
|-
| Nassau
| US 301 (SR 200)
| Duval CL
| Callahan
|}

<h3> Volume Count Updates </h3>

The North Florida TPO provided AADT counts from 2015 FDOT portable traffic monitoring sites at point locations throughout the network. Each count was for total vehicles and did not include directionality or time of day factors. It was assumed that counts on bidirectional roads were equally split between both directions.

These points were spatially joined to the network links and then spot checked to ensure the join was accurate. Particular attention was paid to on and off ramps as they tended to have the least accuracy.

The NERPM-AB has four time periods: AM, midday (MD), PM, and night (NT). The 2010 network contains count data for each of these periods on some links. To create time of day count data for the 2015 counts, the 2015 count was distributed between the four time periods using the same ratio as the 2010 counts. Links with a 2015 count but no 2010 time of day counts used nearby 2010 time of day counts. If no 2010 count was nearby, that 2015 count was not used.

<h2>CUBE Model Update </h2>

Several updates were made to the NERPM-AB Cube model during this project. The previous version of the model had model steps and scripting logic that was left over from the previous four-step model. Therefore, enhancements were made wherever necessary to make sure that the model system and results appear reasonable.

<h3>External-Internal Trips Calibration</h3>

While looking at auxiliary model demand it was found that IE trips are very long – average VMT per trip is approximately 32 miles, compared to 8 miles for internal trips. The VMT needed to be reduced by factoring either or both total IE or truck trips, or by factoring the length of IE trips for which the gravity model used to distribute them needed adjustment. To help inform this decision, the relationship between the observed counts and the estimated link volumes at external stations was investigated. It was found that both ends of I-95 and on I-10 were low, IE trips needed to be increased. It was also possible that the IE trips were traveling too far, which could be adjusted by making the friction factor curve used in trip distribution for IE trips steeper. A series of steps were taken to resolve this issue as discussed in the model documentation.

<h3>Transit Skimming Updates</h3>

From the initial transit assignment results, it was found that the skyway/monorail boardings were significantly lower than observed boardings. Therefore, the next model enhancement made was to add alternative-specific constants (ASC) to DaySim for transit modes based on an in-vehicle time skim. For the transit alternatives that need to be modeled in the scenarios, the in-vehicle time (IVT) needs to be skimmed in a separate skim matrix, and any tour or trip that has the transit mode in the OD Pair should get the constant. The updates are discussed in detail in the model documentation.

<h3>Highway Skimming for Tolls</h3>

In the E+C 2045 networks, express lanes or managed lanes were introduced in the highway network. Initially the toll variable, CARTOLL, was set at zero for all links. This was changed to 20 cents per mile (0.20). This value (CARTOLL) was then multiplied with the link length throughout the model setup to calculate the cost. Moreover, the occupancy for HOV2 and HOV3+ were also changed to 1.3 and 1.5, respectively.

<h2>DaySim Model Calibration and Validation</h2>

The tour destination and other sub models were calibrated using the North Florida Household Travel Survey (2017), the transit tours and trips were calibrated using the Transit On-Board Survey (2016) obtained from Transit On-Board Survey Program, and the worker flow were calibrated using CTPP (2010) obtained from Census.

The following set of tables and figures show calibration summaries:

<h4>Table: Population by Person Type</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! Person type
! Survey
! PopSim
|-
| Full Time Worker
| 540,570
| 566,852
|-
| Part Time Worker
| 96,726
| 59,179
|-
| Retired
| 172,447
| 142,992
|-
| Non-Worker
| 203,794
| 208,490
|-
| University Student
| 43,943
| 55,998
|-
| Student Age 16+
| 37,901
| 48,543
|-
| Student Age 5-15
| 185,288
| 197,734
|-
| Kid under 5
| 83,362
| 102,243
|- style="font-weight:bold;"
| Total
| 1,326,484
| 1,419,578
|}

<h4>Table: Average Home to Work Distance</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! Worker Type
! Survey
! DaySim
|-
| Full Time
| 13.7
| 13.3
|-
| Part Time
| 9.1
| 6.8
|-
| Other
| 9.9
| 14.5
|- style="font-weight:bold;"
| Total
| 13.2
| 12.4
|}

<h4>Table: Workers by County</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! CTPP
! HTS
! DaySim
|-
| 1
| 7,283
| 5,383
| 9,681
|-
| 2
| 59,391
| 80,418
| 84,565
|-
| 3
| 286,647
| 400,012
| 420,057
|-
| 4
| 18,556
| 17,424
| 32,801
|-
| 5
| 13,779
| 10,901
| 20,362
|-
| 6
| 64,325
| 79,338
| 97,402
|- style="font-weight:bold;"
| TOTAL
| 449,980
| 593,476
| 664,868
|}

<h2> Model Calibration and Validation </h2>

The 2017 north florida travel survey and the 2016 onboard rider demographic survey were used to calibrate the model. Traffic counts and transit boarding counts were used to validate the model outputs. The following tables and figures show the results of the calibrated model. These include trip length frequency distributions, tour and trip mode choice model results, highway and transit validation etc.

<h4> Table: Population and Employment </h4>

{| class="wikitable"
|-
! County
! 2015 Population
! 2015 Employment
! BEA 2015
! 2030 Population
! 2030 Employment
! 2045 Population
! 2045 Employment
|-
| Baker
| 23,138
| 9,649
| 9,695
| 32,340
| 15,876
| 37,723
| 20,123
|-
| Clay
| 189,600
| 68,871
| 69,317
| 263,882
| 120,166
| 321,984
| 156,073
|-
| Duval
| 854,757
| 624,952
| 623,596
| 1,078,136
| 786,052
| 1,231,564
| 888,333
|-
| Nassau
| 76,672
| 30,948
| 31,086
| 97,500
| 49,367
| 116,024
| 60,801
|-
| Putnam
| 71,687
| 22,605
| 22,780
| 78,328
| 34,695
| 84,790
| 38,102
|-
| St Johns
| 202,375
| 105,077
| 104,983
| 318,041
| 197,421
| 412,811
| 287,415
|-
| Grand Total
| 1,418,229
| 862,102
| 861,457
| 1,868,227
| 1,203,577
| 2,204,896
| 1,450,847
|}

<h4>Trip Length Frequency Distribution </h4>
[[File:Wsloc.JPG]]

<h4>Tour Mode Choice </h4>
[[File:Tourmode.JPG]]

<h4>Trip Mode Choice </h4>
[[File:Tripmode.JPG]]

<h4>Highway Validation </h4>
[[File:Hwyvalidation.JPG]]

<h4>Transit Validation </h4>
[[File:Trnvalidation.JPG]]

<h2>Software Requirements</h2>

PopulationSim requires the following software, already included in the model setup, and can be found in the following location w.r.t the project folder:

*Anaconda2 with Python 2.7 – “.\User.prg\Population_Synthesis\Anaconda2”

<h2>Setup and Run PopulationSim</h2>

<h3>Setup Directory</h3>

Figure below presents the directory structure for the PopulationSim setup. To set up a PopulationSim run, the user must create the directory structure as shown in below.

<h4>PopulationSim Directory Structure</h4>
[[File:PopSimDir.JPG]]

The folders and files in the directory are explained as follows:

*The data directory holds all the input data like seeds data, control data and crosswalks.
**SCOUNTY Control totals
**TAZ Control totals
**MAZ Control totals
**Geographic Crosswalk
**Household Seed table
**Person Seed table

*The Anaconda2 directory houses the core PopulationSim software files and associated libraries and packages.

*The configs folder contains the settings.yaml file and controls.csv file
**PopulationSim is configured using the settings.yaml file. For this project, it is configured to run in base mode which means it is run from beginning to end and produces a new synthetic population
**controls.csv file specifies all the targets, geography, seed table, control field and their expression to the seed table required for the PopulationSim run.

*The output folder will have the final synthetic household and person file and summary attributes after a successful run.

*This batch file activates the PopulationSim environment and then calls the run_populationsim.py Python script to launch a PopulationSim run.

*PopulationSim is run using the RunPopulationSim.bat batch file in Command Prompt Window. Before starting the run, the path to the Anaconda install must be updated within this file.

The PopulationSim procedure is automated and once you have all the data in place, the user just needs to run the batch file from the command prompt window. To run the PopulationSim for 2030 or 2045 the user need to update the ‘data_dir’ field in the settings.yaml file. The ‘data_dir’ field for different scenario years are:

*2015: The ‘data_dir’ in settings.yaml file should be set to ‘data’

*2030: The ‘data_dir’ in settings.yaml file should be set to ‘data_2030’

*2045: The ‘data_dir’ in settings.yaml file should be set to ‘data_2045’

<h2>Preparing DaySim Inputs</h2>

<h3>Transit Files</h3>

The transit stops file is created using the input transit network from Jacksonville Transit Authority: TROUTE_{Year}{Alt}.LIN
Follow the sequential step below:

*Create a geodatabase (gdb) in Cube

*Import the input transit network file and the highway network file

*Export the nodes (PTNetwork_PTNode) to a shapefile

*Open the shapefile in ArcMap and select the nodes that have STOPNODE=1. These are the transit stops

*Export the selected nodes (stops) to a new stops shapefile. Also Export the transit line file to a shapefile.

*Open the attribute table of the new stops file and add four fields: xcoord_p (double), ycoord_p (double), mode (int) and id (long).

*Calculate xcoord_p and ycoord_p using calculate geometry for the two fields.

*Join transit line by object id and calculate a new field “mode” equal to the MODE field in the line file (1-localbus, 2-skyway). Jacksonville mode codes and daysim mode codes are different, convert these to daysim mode codes before running buffer tools.

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! 2015
! 2030
! 2045
! DaySim
|-
| 21 = LB
| 21 = LB
| 21 = LB
| 1 = LB
|-
| 24 = Skyway (coded as LB)
| 23 = Skyway (coded as LB)
| 23 = Skyway (coded as LB)
| 2 = EB
|-
|
| 27 = CR
| 27 = CR
| 3 = CR
|}

*Calculate stopid as “FID+1”.

*Keep only four fields in the attribute table: id, mode, x_coord, y_coord.

*Export all records in the attribute table to a csv file: nftpo_transitstops.csv

<h2>DaySim Data Editing</h2>

With the base year moving from 2010 to 2015, microzones replacing parcels and new population data, the inputs for DaySim needed to be updated. The following steps describe the DaySim input data preparation and update for the NERPM-AB model including the tool and input data used and output formed.

<h3>Network Data Preparation</h3>

This step calculates “nearby” node pairs of microzones for shortest distance path calculations to be used in DaySim.

Tool: '''Network_DataPrepv2.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*input_node.csv (Node x,ys from an all-streets network)
*jax_MAZs_2015.dat (The coordinates of the newly developed microzones)
*jax_netprep.ctl (Network prep control file)

Output:
*input_od_pairs.csv (for input to shortest path update tool)

<h3>Shortest Path Update</h3>

This process is required to generate more accurate short distances based on an all streets network. DTALite, a dynamic traffic assignment software, is used to generate node-to-node shortest path distances using the all streets network.

Tool: '''DTALite64.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*input_od_pairs.csv (from the Network Data Preparation tool)
*input_node.csv (from all-street network)
*input_link_type.csv (from all-street network)
*input_link.csv (from all-street network)
*DTASettings.ini (settings file)

Output:
*output_shortest_path.txt (for input to Buffering microzones)

<h3>Buffering micro-zones</h3>

The Daysim buffering tool is run to prepare MAZ input file for DaySim. This step calculates the new Microzone buffer measures to be used in DaySim.

Tool: '''DSBuffTool.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*Jax_microzones_2015.csv (Base Microzone file)
*Jacksonville_Intersections.csv (Street intersections file)
*Jacksonville_transitstops.csv (Transit stops file)
*Jacksonville_openspaces.csv (Open spaces/parks file)
*input_node.csv (All-street Network nodes file)
*output_shortest_path.txt (Node-to-node shortest path distance file)

Output:
*buffered_microzone_2015.dat (to be used in DaySim)
*microzonenode.dat (to be used in DaySim)
*output_shortest_path.txt.bin (Change extension using batch file)
*output_shortest_path.txt.index (Change extension using batch file)

The file extension of the last two outputs above need to be changed for reading into DaySim. The last three output files are used in DaySim to estimate short-distances for car, walk and bike trips.

All three DaySim Data tools can be run using RunAll.bat from the command prompt window.

<h3>Running DSBuffTool.exe</h3>

Double-clicking the executable will bring up a GUI as shown in the following figure. The use of this tool is straightforward. The user just needs to specify all the inputs and click on the “Run” button at the bottom of the GUI.

<h4>DaySim Buffering Tool GUI</h4>
[[File:DBTG.JPG]]

In the “INPUT” section of the GUI, distance calculation and buffer type are set to “Euclidean” and “Logistic decay” respectively. These are the recommended settings for running this tool. If node-to-node distances obtained from an all streets network are available, distance calculation may be set to “Node-to-Node”. The default recommended parameters for logistic decay weights (described in the model design chapter) such as buffer decay slope, offset etc., are also automatically populated in the appropriate fields in the GUI.

All other inputs are to specify file paths that can be done by clicking on the “Browse” button. This pops a file dialog as show in the figure below. The user may also enter full file paths manually using keyboard.

<h4>Buffer Tool File Selection Dialog</h4>
[[File:BTFSD.JPG]]

All file specification fields for the tool are described below. Details about formats for the input files can be found in the directory and data structures chapter.

*MAZ Data File: This is the base MAZ file. It is obtained by running the disaggregation tool.

*Intersection Data File: This file has the coordinates of intersections and the number of links intersecting at each one of them.

*Transit Stops File: This file has the transit stop location coordinates by transit sub-mode.

*Open Spaces File: This file has the locations and area of parks/open spaces in the model region.

*Circuity Data File: This is only required for “Circuity” distance calculation that is now an obsolete method.

When “Node-to-Node” distance calculation is selected, the user need to select a format of the input node-node distance file: output of DTALite or input file used in DaySim. The following files are only needed for “Node-to-Node” distance calculation method.

*Node Data File: This file contains the coordinates of node from an all streets network.

*Node to Node Distance File: This file contains the network shortest path distances between a list of node pairs that are within 3 miles of each other. It is either an output from DTALite or a distance file input to DaySim.

*MAZ-Node Correspondence File: This is an intermediate file created during the buffering process and contains the correspondence between MAZs and nodes.

*Open spaces-Node Correspondence File: This is an intermediate file created during the buffering process and contains the correspondence between open spaces and nodes.

Specification of the following XML files is optional. However, they store the input configuration of a buffering tool run and make it convenient to use the tool.

*Output XML File: If a file is specified in this field, an XML file containing all the input information specified by the user is stored upon clicking on “Run” button that initiated the buffer process.

*XML Input File: Once an XML file has been stored as described above, it can be selected to automatically populate all the input fields required for a buffer run. This can be used to re-run a buffer process with the same inputs or modify a few inputs to generate a different buffered MAZ file.

Finally, Buffered Output File field is used to specify the location and name of the buffered microzone output file that needs to go into DaySim inputs folder.

<h3>DaySim Files Update</h3>

The following files were updated in the latest 2015 DaySim run:

*New Microzone file containing aggregated household and employment inputs and buffered measures instead of parcels

*Household file containing all household information with respect to the microzones

*DaySim Configuration file containing the DaySim execution settings

*A set of updated DaySim input files

*A set of new Data files for short distance calculation

To accurately calculate short car, walk and bike trips, short distance node-to-node measure is introduced in DaySim. This includes the following changes:

*Change in DaySim Configuration file

*Utilization of three additional files in DaySim:
**Microzone node file (microzonenode.dat)
**Node Index file (output_shortest_path_txt_index.dat)
**Node distance file (output_shortest_path_txt_bin.dat)

<h3>Activating TAZ</h3>

The model contains a list of TAZs that are dummy TAZs that act as a placeholder to be later used when building new TAZs. The steps to utilize or activate these dummy zones is as follow:

*Identify the dummy TAZ that needs to be activated.

*Update the input network MicroCodedHnet42.net within Cube.
**Relocate the dummy TAZ to the centroid of new TAZ.
**Update the attributes of the dummy TAZ.
**Build network links that would connect the dummy TAZ to appropriate nodes on the network.
**Update the attributes of these network links.

*Identify the overlap/association between existing MAZs and the new TAZ.

*Update the land-use file nftpo_microzones_year.csv.
**Update the “taz_p” field for MAZs that are now identified as associated with new TAZ for all scenario years.

*Update the PopulationSim Inputs.
**Update the fields associated with MAZs identified in the earlier step with updated TAZ in geo_cross_walk.csv.
**Update the TAZ field for identified MAZs in control_totals_maz.csv file.
**Update the control_totals_taz.csv file by adding a row for dummy TAZ and recording appropriate values in rest of the fields. It should be ensured that the values entered are reasonable for e.g. household size values should not all be 0 and should match up to total households.
**Go through the steps described in Updating Land-use Manually later in this document.

*Update scenario input files in “scenario/Input/” folder.
**Add/Update the “Zone_id” and “Zone_ordinal” field in the _jax_taz_indexes.dat file located in “scenario/Input/DaySimInput/01_TAZ_Index/” folder to the dummy TAZ. Set the “Dest_eligible” field for this TAZ to a value of 1.
**Add/Update the TAZ field in the emp_year.dbf file located in “scenario/Input/DaySimInput/02_Parcel/” folder.
**Add/Update the TAZ field in the _jax_worker_ixxifractions.dat file located in “scenario/Input/DaySimInput/05_ixxi/” folder.
**Add/Update the TAZ field in the county_districts.csv file located in the “scenario/Input/DaySimInput/08_Summaries/” folder.
**Add/Update the TAZ field in the RIVERCROSS.csv file located in the “scenario/Input/” folder.

This concludes the steps needed to activate a dummy TAZ, and the use can now run the regular model.

<h3>Updating Land-use Manually</h3>

When updating land-use data, like household or population attributes, for the base or future year, the steps below should be followed to ensure proper propagation of changes throughout the model:

*The starting point for such changes is the buffering micro-zones step. One of the inputs to the buffering tool is the MAZ file, for example, nftpo_microzones_2015.csv for the 2015 scenario. The user should manually update the land-use file for changes required in specific zones because of new development, changed land-use, etc. The user should then run the buffer tool and copy the output file (buffered_maz_2015.dat) to the scenario input directory.
*The next step is to update the PopulationSim controls by following the guidelines in Section 3.1 for the base year or in Appendix A (Updating Control Data). The user should then run PopulationSim.

*The user should then run the “popsim_to_daysim.bat” file by double clicking it in Windows or running it from a DOS command promt. The batch file runs the “convert.py” script that converts PopulationSim output to DaySim inputs. The DaySim inputs are output in the “./User.prg/PopulationSim/popsim_to_daysim/output/” folder. The output should be renamed as shown in Table 78. These outputs should be copied to the model scenario input directory.

<h4>Table: Rename popsim_to_daysim.bat output</h4>

{| class="wikitable" style="font-weight:bold; text-align:center; vertical-align:middle;"
|-
! rowspan="2" | Output File
! colspan="3" | Rename File
|-
| Base2015
| INT2030
| CF2045
|- style="font-weight:normal; text-align:left;"
| household_2015.dat
| household_2015.dat
| household_2030.dat
| household_2045.dat
|- style="font-weight:normal; text-align:left;"
| person_2015.dat
| person_2015.dat
| person_2030.dat
| person_2045.dat
|}

This concludes the land-use updates step, and the user can now run the regular model.

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Bstabler:

7.3 Version NERPMAB2

2020-12-08T02:20:41Z

Bstabler: 7.3 Version NERPMAB2v1 major updates from the model document

[[Category:7.0 Version Updates]]
'''7.3 VERSION NERPM-AB2'''

This section documents the set of improvements that were made to the NERPM model for the release of NERPM-AB2. The improvements focused on:

*Base year updated from 2010 to 2015
*Re-built all land use inputs for 2015 and future
*Switched from parcels to microzones to ease data preparation
*Switched from PopGen to PopulationSim
*Updated the highway network to 2015 (NFTPO)
*Updated the transit network to 2016 (JTA)
*Updated external traffic volumes and distributions based on Bluetooth OD data (FDOT)
*Upgraded from the NFTPO specific version of DaySim to the multiple-agency (a.k.a. core) version, which is faster, more stable, and includes new features such as Transportation Network Companies (TNC) mode (i.e. Uber and Lyft)
*Updated future year scenarios

<h2> Parcel to Microzone (MAZ) </h2>

In the previous version of the model, the unit of spatial analysis was parcels. There were approximately 700,000 parcels in the region, which made it difficult to maintain, edit and attribute, both the existing and alternative scenarios. This led to the creation and use of microzones as an alternative, which is halfway between parcels and TAZs and are easier to maintain. As you can see in the table, there are around 55 thousand microzones against 700 thousand parcels. This makes it much easier for the user to update land use information such as employment, enrollment etc. The total number of TAZs in this version is 1862. The plot shows the TAZ and the MAZ layer around downtown Jacksonville. This shows how TAZs and MAZs are related. You can see in the plot that MAZs in downtown tend to be smaller than the ones in the periphery. If you have any questions about the development of MAZs, please refer to the model documentation for more details.
{| class="wikitable"
|-
! <br />County
! <br />Number of Parcels
! <br />% Freq
! <br />Number of MAZ
! <br />% Freq
! <br />Number of TAZ
! <br />% Freq
|-
| <br />Baker
| <br />12,490
| <br />1.78
| <br />1,735
| <br />3.14
| <br />29
| <br />1.56
|-
| <br />Clay
| <br />84,529
| <br />12.02
| <br />7,796
| <br />14.13
| <br />184
| <br />9.88
|-
| <br />Duval
| <br />355,805
| <br />50.59
| <br />28,263
| <br />51.22
| <br />1,281
| <br />68.80
|-
| <br />Nassau
| <br />47,443
| <br />6.75
| <br />4,455
| <br />8.07
| <br />108
| <br />5.80
|-
| <br />Putnam
| <br />102,053
| <br />14.51
| <br />6,652
| <br />12.05
| <br />44
| <br />2.36
|-
| <br />St. Johns
| <br />100,950
| <br />14.35
| <br />6,283
| <br />11.39
| <br />216
| <br />11.60
|-
| <br />Total
| <br />703,270
| <br />100.00
| <br />55,184
| <br />100.00
| <br />1,862
| <br />100.00
|}

<h4> Figure: MAZ and TAZ layer in Downtown Jacksonville </h4>

[[File:Maztaz.jpg]]

<h2> Land-use data updated to base year 2015 </h2>

Once the microzones were formed the following land-use data were updated for the base year 2015:

<h3> Household Data </h3>

The household data at parcel level was aggregated to microzone level for the base year 2015.

<h3> Employment Data </h3>

The employment data was updated using three sources of data:

*The 2010 parcel employment data projected to 2015

*Data provided by Infogroup and edited by North Florida TPO

*Data provided by Cambridge Systematics as described in their June 30, 2017 memorandum “Development of 2015 Employment Data” for FDOT

Please refer to the model documentation for the details on how these data sources were utilized.

<h3> School Enrollment Data </h3>

School enrollment data from the Florida Department of Education (FDOE) provided by North Florida TPO was used to develop school enrollment data at microzone level for the base year 2015. The details of this development process can be found in the model documentation.

<h3> Parking </h3>

The parking data was derived from Florida DOT parking shapefiles "Parking surface" and "g100freeway". The website [https://www.bestparking.com/jacksonville-fl-parking/ Jacksonville, FL Parking] was also used along with the shapefiles to develop parking data.

<h3> Lodging </h3>

A list of 367 known hotels, motels, and bed and breakfasts from the Florida Department of Business and Professional Regulation provided by North Florida TPO was used to develop lodging information.

<h3> Special Generators </h3>

Jacksonville International Airport and St. Augustine are the two special generators that are effectively acting as a special generators in the model.

The Airport had an attraction value of 15,000 in 2010. Based on several sources, RSG determined that the passenger traffic at the airport has decreased slightly between 2010 and 2015. CAPA – Centre for Aviation noted an overall downward trend in traffic since 2008 but traffic began to pick up in 2013 . The airport’s website lists the 2010 and 2015 number of passengers as 5,602,000 and 5,502,000 respectively, which represents a 1.8% reduction in traffic. FDOT’s 2015 Airport Profile of JAX showed similar findings. Given the small change over the years, we recommend leaving the airport’s attraction value at 15,000 for 2015.

St. Augustine had an attraction value of 2,288 in 2010. This number accounts for tourists coming to the area. In 2010 and 2015, St. Johns County had 3.4 million and 6.3 million visitors, respectively, which represents an 85% growth over the five-year period. The 2015 number was considered an anomaly because it was the City’s 450th birthday. However, 2016 was even higher with 6.8 million visitors, so an 85% increase may be reasonable. Increasing the attractions value by 85% to 4,240 could have a noticeable impact on links near the TAZ.

<h2> PopulationSim </h2>

*PopulationSim is an open platform for population synthesis

*Replaced PopGen in this version of NERPM-AB

*Has better demographic and geographic methods compared to PopGen

*Actively maintained by the travel modeling community

*Run only when there are major changes in the landuse data

[http://https://activitysim.github.io/populationsim/# PopulationSim Wiki]

<h3> Input Data </h3>

The main inputs to a population synthesizer are

*Disaggregate population samples (Seed Sample)

*Marginal control distributions (Control variables)

The Seed sample is obtained from the [https://www.census.gov/programs-surveys/acs/data/pums.html Census Public Use Microdata Sample (PUMS)] and the Marginal distributions of person and household attributes are obtained from [https://www.bebr.ufl.edu/ Bureau of Economic and Business Research (BEBR)] and Census respectively.

The next step is the preparation of inputs to PopulationSim which includes:

<h4> Geographic cross-walk </h4>

PopulationSim can handle any number of nested geographies. The geography level (hierarchy) selected for NERPM-AB 2015 to which the marginal distributions are specified are as follows:

*Meta Geography: Super County (SCOUNTY)

*Seed Geography: PUMA

*Sub-seed Geography: TAZ and MAZ

Super-County is chosen instead of County due to overlapping of county and PUMA boundaries. Super County is the combination of multiple County to form one geographic area.

<h4> Seed population (Household and Person tables) </h4>

One of the main requirements for the seed sample is that it should be representative of the modeling region. The seed sample is obtained from PUMS dataset. The PUMS data contain five years of household records. The seed sample should be representative of the modeling region. It must contain all the specified control variables, as well as any variables that are needed for the travel model but not specified as controls. The PUMS data is downloaded from PUMS website and it is extracted both demographically and geographically for the North Florida TPO region using PUMA codes conforming to the region. There are 11 PUMA regions in the North Florida TPO area. The seed sample must include an initial weight field. The model setup includes the Seed sample that contains a weight field, WGTP, which is used for control of total households.

<h4> Controls </h4>

Controls or targets are the marginal distributions that form the constraints for the population synthesis procedure. The objective of the population synthesis procedure is to produce a synthetic population with attributes matching these marginal distributions. The controls were developed at three geographic levels:

<h5> Super County </h5>

The raw input data was downloaded at county level from [https://www.bebr.ufl.edu/sites/default/files/Research%20Reports/projections_2016_asrh.xlsx BEBR].
Next, the data was aggregated to super county level. The data at super county level are person level attributes:

*Male

*Female

*Age 0-4

*Age 5-17

*Age 18-24

*Age 25-54

*Age 55+

<h6> Table: BEBR Super County Totals (2015) </h6>

{| class="wikitable"
|-
! SCOUNTY
! PERSONS
! MALE
! FEMALE
! AGE0-4
! AGE5-17
! AGE18-24
! AGE25-54
! AGE55+
|-
| 12109107
| 286,322
| 140,049
| 146,273
| 15,257
| 47,539
| 23,702
| 104,701
| 95,123
|-
| 12031000
| 905,574
| 440,059
| 465,515
| 61,304
| 146,443
| 87,517
| 379,741
| 230,569
|-
| 12003089
| 103,553
| 51,969
| 51,584
| 5,963
| 16,366
| 8,505
| 38,968
| 33,751
|-
| 12019000
| 201,277
| 98,400
| 102,877
| 12,543
| 37,662
| 18,453
| 79,447
| 53,172
|}

<h5> TAZ </h5>

The raw input data was downloaded at various levels [Source: 2011-15 American Community Survey five-year estimates, ACS5] and it was processed at TAZ level to generate household attributes:

*Households by Household Size at Block Group Level
**Household Size 1
**Household Size 2
**Household Size 3
**Household Size 4+

*Tenure by Age of Householder at Block Group Level
**Householder Age 15-44
**Householder Age 45-64
**Householder Age 65+

*Households by number of workers at Census Tract Level
**Zero household workers
**One household workers
**Two household workers
**Three or more than three household workers

*HHs by income at Census Tract Level
**Household income $0-$24,999
**Household income $25,000-$59,999
**Household income $60,000-$99,999
**Household income $100,000+

<h5> MAZ </h5>

The raw input data was obtained at parcel level for the year 2010 and calculated for 2015 using growth factor from 2010 to 2015. The data obtained is total number of households and then aggregated for each MAZ. Once the control files are prepared, the total number of households and persons are calculated across geographies to check for consistency as shown in Table below.

<h5> Table: Control data summary (2015) </h5>

{| class="wikitable" style="font-weight:bold; vertical-align:middle;"
|- style="text-align:center;"
! style="text-align:left;" | Data
! Geography
! Value
|-
| Total number of households
| style="text-align:center;" | MAZ
| style="font-weight:normal;" | 582,199
|-
| Total number of households
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 582,199
|-
| Number of households in HHSIZE=1
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 155,522
|-
| Number of households in HHSIZE=2
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 196,050
|-
| Number of households in HHSIZE=3
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 87,996
|-
| Number of households in HHSIZE=4+
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 110,821
|-
| Total number of persons (from HHSIZE)
| style="text-align:center;" | TAZ
| style="font-weight:normal;" | 1,397,535
|-
| Total number of persons
| style="text-align:center;" | SCOUNTY
| style="font-weight:normal;" | 1,397,534
|}

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev.jpg]][[File:Prmse.jpg]]

<h2> Development of 2045 land-use data </h2>

The 2045 land-use data was created by applying a methodology, detailed in the model documentation, to the land-use data for the base year (2015) and the BEBR forecasts. The table below shows the summary of comparisons between land-use data for the base year (2015) and the future year (2045).

<h4> Table: Comparison of 2015 and 2045 totals </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! Households<br />2015
! Households<br />2045
! Employment<br />2015
! Employment<br />2045
! Enrollment<br />2015
! Enrollment<br />2045
|-
| NASSAU
| 32,983
| 51,902
| 28,480
| 55,393
| 13,368
| 31,576
|-
| DUVAL
| 357,463
| 541,730
| 496,394
| 708,478
| 211,091
| 362,983
|-
| ST JOHNS
| 84,540
| 181,500
| 66,355
| 179,066
| 43,977
| 116,687
|-
| CLAY
| 70,523
| 132,830
| 46,539
| 105,158
| 40,877
| 98,998
|-
| BAKER
| 8,815
| 15,117
| 8,909
| 18,491
| 5,128
| 9,876
|-
| PUTNAM
| 31,278
| 40,307
| 21,521
| 35,564
| 14,856
| 23,404
|- style="font-weight:bold;"
| TOTAL
| 585,602
| 963,386
| 668,198
| 1,102,150
| 329,297
| 643,524
|}

<h2> Development of 2045 control data </h2>

Controls at three levels of geography are required to run PopulationSim for North Florida TPO: Super County, TAZ and MAZ. The Super COUNTY level controls were obtained from BEBR data and the TAZ and MAZ level controls were prepared using the 2045 land-use data. Please refer to the model documentation for the details of this development process.

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev2045.jpg]]

<h2> Updating 2045 control data </h2>

When updating land-use data it is necessary to update the PopulationSim controls. The following example shows how to update the controls when adding 100 households to MAZ 1439:

<h4> Land-use file before update </h4>
[[File:lubefore45.jpg]]

*Update the land-use “nftpo_microzones_2045.csv” file by adding 100 households to MAZ 1439. The figure above shows the land-use file before the addition of 100 households and the figure below shows the land use file after the addition of 100 households.

<h4> Land-use file after update </h4>
[[File:luafter45.jpg]]

*Edit the variables listed in the table below within the CUBE script “update_controls.S” located in “./User.prg/Population_Synthesis/” folder. The script performs following actions:
**Makes a copy of control files.
**Updates MAZ level household controls by comparing existing control values with land-use values.
**Updates TAZ level household controls by aggregating land-use values by TAZ. Redistributes the additional households by household size based on the distribution of households by household size as observed in control data. Similarly redistributes additional household based on distribution of households by householder age, number of household workers, and household income respectively as observed in the control data.
**Updates County level population controls by adding additional person based on average household size calculated for each county from existing controls. The additional population is distributed across gender and age group based on distribution of population observed in controls for respective categories.

<h4>Table: Variables to update by user when updating update_control.S script </h4>

{| class="wikitable"
|-
! VARIABLE NAME
! DESCRIPTION
! EXAMPLE
|-
| LANDUSE_PATH
| Full path to the folder that contains the land-use file.
| 'E:\NERPMABv2.0\User.prg\DaySim_Data_Tools'
|-
| LANDUSE_FILE
| Name of the land-use file
| 'nftpo_microzones_2045.csv'
|-
| POPSIM_PATH
| Full path to the PopulationSim folder
| 'E:\NERPMABv2.0\User.prg\Population_Synthesis'
|-
| CONTROL_FOLDER
| Name of the folder containing the control data
| 'data_2045'
|-
| OVERWRITE_INITIAL
| A value of 0 will not overwrite the initial files and a value of 1 will overwrite the initial files.
| 0: Does not overwrite the control_totals_{zone}_initial.csv file<br />1: Overwrites the control_totals_{zone}_initial.csv file
|}

*Run “update_controls.bat” file, located in “./User.prg/Population_Synthesis/” folder, by double-clicking. This will call the Cube Voyager and run the “update_controls.S” script.
*The control files are now updated and are ready to be used by PopulationSim.

<h2> Development of 2030 land-use data </h2>

The 2030 land-use data was created by applying a methodology, detailed in the model documentation, to the land-use data for the base year (2015) and the BEBR forecasts. The table below shows the summary of comparisons between land-use data for the base year (2015) and the future year (2030).

<h4> Table: Comparison of 2015 and 2030 totals </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! Households<br />2015
! Households<br />2030
! Employment<br />2015
! Employment<br />2030
! Enrollment<br />2015
! Enrollment<br />2030
|-
| NASSAU
| 32,983
| 41,948
| 28,480
| 45,394
| 13,368
| 25,998
|-
| DUVAL
| 357,463
| 453,184
| 496,394
| 622,922
| 211,091
| 311,765
|-
| ST JOHNS
| 84,540
| 135,295
| 66,355
| 124,742
| 43,977
| 79,721
|-
| CLAY
| 70,523
| 100,980
| 46,539
| 81,119
| 40,877
| 75,838
|-
| BAKER
| 8,815
| 12,212
| 8,909
| 14,615
| 5,128
| 7,602
|-
| PUTNAM
| 31,278
| 35,535
| 21,521
| 32,993
| 14,856
| 22,076
|- style="font-weight:bold;"
| TOTAL
| 585,602
| 779,154
| 668,198
| 921,785
| 329,297
| 523,000
|}

<h2> Development of 2030 control data </h2>

Controls at three levels of geography are required to run PopulationSim for North Florida TPO: Super County, TAZ and MAZ. The Super COUNTY level controls were obtained from BEBR data and the TAZ and MAZ level controls were prepared using the 2030 land-use data. Please refer to the model documentation for the details of this development process.

In this figure, we see the performance of PopulationSim as it tries to match the controls. In this version of the model, controls such as household size, householder age, household income etc. were used at different levels of geography. The standard deviation (STDEV) of the percentage difference informs us of how much dispersion from the average exists. As you can see, the total number of households at the MAZ level matches perfectly with the controls. PopulationSim does that by design. And we see a very close match on all the other controls as well.

[[File:Sdev2030.jpg]]

<h2> Updating 2030 control data </h2>

When updating land-use data it is necessary to update the PopulationSim controls. The following example shows how to update the controls when adding 100 households to MAZ 1439:

<h4> Land-use file before update </h4>
[[File:lubefore30.jpg]]

*Update the land-use “nftpo_microzones_2045.csv” file by adding 100 households to MAZ 1439. The figure above shows the land-use file before the addition of 100 households and the figure below shows the land use file after the addition of 100 households.

<h4> Land-use file after update </h4>
[[File:luafter30.jpg]]

*Edit the variables listed in the table below within the CUBE script “update_controls.S” located in “./User.prg/Population_Synthesis/” folder. The script performs following actions:
**Makes a copy of control files.
**Updates MAZ level household controls by comparing existing control values with land-use values.
**Updates TAZ level household controls by aggregating land-use values by TAZ. Redistributes the additional households by household size based on the distribution of households by household size as observed in control data. Similarly redistributes additional household based on distribution of households by householder age, number of household workers, and household income respectively as observed in the control data.
**Updates County level population controls by adding additional person based on average household size calculated for each county from existing controls. The additional population is distributed across gender and age group based on distribution of population observed in controls for respective categories.

<h4>Table: Variables to update by user when updating update_control.S script </h4>
{| class="wikitable"
|-
! VARIABLE NAME
! DESCRIPTION
! EXAMPLE
|-
| LANDUSE_PATH
| Full path to the folder that contains the land-use file.
| 'E:\NERPMABv2.0\User.prg\DaySim_Data_Tools'
|-
| LANDUSE_FILE
| Name of the land-use file
| 'nftpo_microzones_2030.csv'
|-
| POPSIM_PATH
| Full path to the PopulationSim folder
| 'E:\NERPMABv2.0\User.prg\Population_Synthesis'
|-
| CONTROL_FOLDER
| Name of the folder containing the control data
| 'data_2030'
|-
| OVERWRITE_INITIAL
| A value of 0 will not overwrite the initial files and a value of 1 will overwrite the initial files.
| 0: Does not overwrite the control_totals_{zone}_initial.csv file<br />1: Overwrites the control_totals_{zone}_initial.csv file
|}

*Run “update_controls.bat” file, located in “./User.prg/Population_Synthesis/” folder, by double-clicking. This will call the Cube Voyager and run the “update_controls.S” script.

*The control files are now updated and are ready to be used by PopulationSim.

<h2> DaySim ABM Model Update </h2>

After completion of base year 2015 data aggregation from parcels to microzones, DaySim was tested for the new geographies and the results compared against the parcel results to check for consistencies. The necessary input files were created for DaySim run as explained in detail in Appendix B of the model documentation. After a successful run of DaySim using updated 2015 Microzone and Population synthesis dataset, the results were compared with previous DaySim run using parcels. The small set of tables and figures displayed here show the consistency of the 2015 dataset in DaySim performance. A complete series of tables and figures are shown in the model documentation.

<h4> Table: Estimated Households by Number of Vehicles and County (Parcel) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 309
| 2,750
| 3,899
| 1,285
| 557
| 8,800
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 2%
|-
| Clay
| 2,154
| 19,501
| 32,064
| 11,436
| 4,123
| 69,278
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 12%
|-
| Duval
| 27,194
| 126,487
| 137,367
| 42,231
| 15,633
| 348,912
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 61%
|-
| Nassau
| 1,212
| 10,006
| 14,514
| 4,681
| 1,820
| 32,233
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 6%
|-
| Putnam
| 1,594
| 11,944
| 12,800
| 3,767
| 1,543
| 31,648
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 6%
|-
| St. Johns
| 3,097
| 25,256
| 36,209
| 11,566
| 4,102
| 80,230
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 14%
|- style="font-weight:bold;"
| Total
| 35,560
| 195,944
| 236,853
| 74,966
| 27,778
| 571,101
| style="text-align:right; vertical-align:bottom; background-color:#FBD1A5; color:#48484A;" | 100%
|}

<h4> Table: Estimated Households by Number of Vehicles and County (Microzone + Node to Node Distances) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 333
| 2,781
| 3,868
| 1,261
| 557
| 8,800
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 2%
|-
| Clay
| 2,491
| 19,945
| 31,772
| 11,122
| 3,948
| 69,278
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 12%
|-
| Duval
| 28,867
| 126,963
| 136,315
| 41,659
| 15,108
| 348,912
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 61%
|-
| Nassau
| 1,329
| 10,195
| 14,426
| 4,515
| 1,768
| 32,233
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 6%
|-
| Putnam
| 1,627
| 11,994
| 12,767
| 3,750
| 1,510
| 31,648
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 6%
|-
| St. Johns
| 3,795
| 25,835
| 35,721
| 11,105
| 3,774
| 80,230
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 14%
|- style="font-weight:bold;"
| Total
| 38,442
| 197,713
| 234,869
| 73,412
| 26,665
| 571,101
| style="text-align:right; vertical-align:bottom; background-color:#C1E1BF; color:#48484A;" | 100%
|}

<h4> Table: Estimated Households by Number of Vehicles and County (Microzone + N2N Distances + PopSim 2015) </h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! rowspan="2" | County
! colspan="7" | Number of Vehicles
|- style="font-weight:bold;"
| 0
| 1
| 2
| 3
| style="text-align:right;" | 4+
| style="text-align:right;" | Total
| style="text-align:right;" | % Total
|-
| Baker
| 403
| 3,021
| 3,704
| 1,015
| 672
| 8,815
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 2%
|-
| Clay
| 2,742
| 21,083
| 31,758
| 11,032
| 3,908
| 70,523
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 12%
|-
| Duval
| 29,793
| 131,763
| 131,610
| 44,856
| 16,038
| 354,060
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 61%
|-
| Nassau
| 1,471
| 12,051
| 13,617
| 3,469
| 2,375
| 32,983
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 6%
|-
| Putnam
| 2,158
| 13,690
| 10,852
| 3,059
| 1,519
| 31,278
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 5%
|-
| St. Johns
| 3,652
| 28,418
| 37,278
| 11,588
| 3,604
| 84,540
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 15%
|- style="font-weight:bold;"
| Total
| 40,219
| 210,026
| 228,819
| 75,019
| 28,116
| 582,199
| style="text-align:right; vertical-align:bottom; background-color:#73D3FF; color:#48484A;" | 100%
|}

<h4>Work Location Choice: HOME-WORK DISTANCES </h4>
[[File:WLCHWD.JPG]]

<h4>School Location Choice: HOME-SCHOOL DISTANCES </h4>
[[File:SLCHSD.JPG]]

<h2>Development of External Model Inputs </h2>

<h3>Data Sources </h3>

The source data for the X-X trip table has been obtained from Bluetooth detectors that were installed at 17 locations adjacent to highways at external model stations. This work was conducted under a separate contract with Florida DOT. The raw data from the Bluetooth deployment were obtained by HNTB and processed for further analysis by RSG.

*2010 North Florida TPO model X-X trip table (EETRIPS.dbf): (29 nodes)
The total trips originating for each external node and destined for other external nodes are calculated by assigning a proportion of the total incoming traffic (e.g. 16%). Incoming traffic is distributed from each origin external node to each other external node in proportion to the destination node’s AADT.

*EE_O-D_Study spreadsheet provided by HNTB: (17 nodes)
This spreadsheet was provided by HNTB and contains the raw matched BT X-X data, total traffic counts, total truck counts, matched BT X-X/X-I data (17 external and 150 internal nodes), and related pivot tables.

<h3>Estimating the BT Expansion Factor </h3>

The BT expansion factor (or penetration rate) is the percentage of total traffic recorded with having a discoverable Bluetooth device on or within the vehicle. The expansion factors imputed from the HNTB data were abnormally high given RSG’s prior experience in working with data of this type. RSG requested, but never received from HNTB, the raw Bluetooth data. For this reason, RSG developed a method to estimate an expansion factor for each station. The details of the method can be found in the model documentation.

<h3>Estimating External-External Trips </h3>

The raw matched X-X BT data alongside with expansion factors are used to estimate the X-X trips. The data is cleaned based on methodology described in the model documentation.

<h2>Network Updates </h2>

<h3>Network Link Updates </h3>

The previous Existing + Committed (E+C) network, known as 2040A in the 2010 model setup, was used as the starting point for the 2015 network. Table below lists the previous E+C network links that were modified to be consistent with 2015 base year conditions. Modifications included revisions to the number of lanes, facility type, and turn restrictions.

UPDATE (01/30/2020): BUSTFAC_45C was set to 0 in the 2045 network fields. This was causing problems with transit assignment resulting in very low transit ridership. RSG set this to 1 for all links since BUSTFAC_15A has it set to 1 for all links. This resolved the low transit ridership issue in the scenario year 2045.

Before setting up a model run, please make sure that in the transit route input file (or any subsequent transit route input files), the skyway modes are coded as MODE equal 24 instead of 23 and commuter rail “CR_SE-Rail” is coded as MODE equal 25 instead of 27 to be consistent with the new in-vehicle time skims.

<h4>E+C Network (2040A) Links to Change for 2015 Network</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! County
! Route
! From
! To
|-
| Nassau
| Chester Rd.
| SR A1A
| Green Pine Rd.
|-
| Duval
| First Coast Expwy. (SR 23)
| Clay CL
| Argyle Forest Blvd.
|-
| Duval
| First Coast Expwy. (SR 23)
| Argyle Forest Blvd
| I-10
|-
| Clay
| First Coast Expwy. (SR 23)
| Blanding Blvd (SR 21)
| Duval CL
|-
| Duval
| I-10 @ I-95
| Roosevelt Ave. (US 17)
| San Marco Ave.
|-
| Duval
| I-295
| SR 9B
| J.T. Butler Blvd. (SR 202)
|-
| Duval
| I-295
| I-10
| Commonwealth Ave.
|-
| Duval
| I-295
| Buckman Bridge
| I-95
|-
| Duval
| I-295
| I-95 (South)
| SR 9B
|-
| Duval
| Martin Luther King Jr. Pkwy
| at 21st St./Talleyrand Ave.
|
|-
| Duval
| SR 9B
| Philips Hwy. (US1)
| I-295
|-
| Duval
| SR 9B
| I-95
| Philips Hwy. (US1)
|-
| Duval
| US 301 (SR 200)
| South of Baldwin
| North of Baldwin
|-
| Duval
| Blanding Blvd/SR 21
| South of Old Jennings
| North of CR 218
|-
| Duval
| I-10 @ Beaver Street
| Remove access to Beaver Street
|
|-
| Nassau
| US 301 (SR 200)
| Duval CL
| Callahan
|}

<h3> Volume Count Updates </h3>

The North Florida TPO provided AADT counts from 2015 FDOT portable traffic monitoring sites at point locations throughout the network. Each count was for total vehicles and did not include directionality or time of day factors. It was assumed that counts on bidirectional roads were equally split between both directions.

These points were spatially joined to the network links and then spot checked to ensure the join was accurate. Particular attention was paid to on and off ramps as they tended to have the least accuracy.

The NERPM-AB has four time periods: AM, midday (MD), PM, and night (NT). The 2010 network contains count data for each of these periods on some links. To create time of day count data for the 2015 counts, the 2015 count was distributed between the four time periods using the same ratio as the 2010 counts. Links with a 2015 count but no 2010 time of day counts used nearby 2010 time of day counts. If no 2010 count was nearby, that 2015 count was not used.

<h2>CUBE Model Update </h2>

Several updates were made to the NERPM-AB Cube model during this project. The previous version of the model had model steps and scripting logic that was left over from the previous four-step model. Therefore, enhancements were made wherever necessary to make sure that the model system and results appear reasonable.

<h3>External-Internal Trips Calibration</h3>

While looking at auxiliary model demand it was found that IE trips are very long – average VMT per trip is approximately 32 miles, compared to 8 miles for internal trips. The VMT needed to be reduced by factoring either or both total IE or truck trips, or by factoring the length of IE trips for which the gravity model used to distribute them needed adjustment. To help inform this decision, the relationship between the observed counts and the estimated link volumes at external stations was investigated. It was found that both ends of I-95 and on I-10 were low, IE trips needed to be increased. It was also possible that the IE trips were traveling too far, which could be adjusted by making the friction factor curve used in trip distribution for IE trips steeper. A series of steps were taken to resolve this issue as discussed in the model documentation.

<h3>Transit Skimming Updates</h3>

From the initial transit assignment results, it was found that the skyway/monorail boardings were significantly lower than observed boardings. Therefore, the next model enhancement made was to add alternative-specific constants (ASC) to DaySim for transit modes based on an in-vehicle time skim. For the transit alternatives that need to be modeled in the scenarios, the in-vehicle time (IVT) needs to be skimmed in a separate skim matrix, and any tour or trip that has the transit mode in the OD Pair should get the constant. The updates are discussed in detail in the model documentation.

<h3>Highway Skimming for Tolls</h3>

In the E+C 2045 networks, express lanes or managed lanes were introduced in the highway network. Initially the toll variable, CARTOLL, was set at zero for all links. This was changed to 20 cents per mile (0.20). This value (CARTOLL) was then multiplied with the link length throughout the model setup to calculate the cost. Moreover, the occupancy for HOV2 and HOV3+ were also changed to 1.3 and 1.5, respectively.

<h2>DaySim Model Calibration and Validation</h2>

The tour destination and other sub models were calibrated using the North Florida Household Travel Survey (2017), the transit tours and trips were calibrated using the Transit On-Board Survey (2016) obtained from Transit On-Board Survey Program, and the worker flow were calibrated using CTPP (2010) obtained from Census.

The following set of tables and figures show calibration summaries:

<h4>Table: Population by Person Type</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! Person type
! Survey
! PopSim
|-
| Full Time Worker
| 540,570
| 566,852
|-
| Part Time Worker
| 96,726
| 59,179
|-
| Retired
| 172,447
| 142,992
|-
| Non-Worker
| 203,794
| 208,490
|-
| University Student
| 43,943
| 55,998
|-
| Student Age 16+
| 37,901
| 48,543
|-
| Student Age 5-15
| 185,288
| 197,734
|-
| Kid under 5
| 83,362
| 102,243
|- style="font-weight:bold;"
| Total
| 1,326,484
| 1,419,578
|}

<h4>Table: Average Home to Work Distance</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! Worker Type
! Survey
! DaySim
|-
| Full Time
| 13.7
| 13.3
|-
| Part Time
| 9.1
| 6.8
|-
| Other
| 9.9
| 14.5
|- style="font-weight:bold;"
| Total
| 13.2
| 12.4
|}

<h4>Table: Workers by County</h4>

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! COUNTY
! CTPP
! HTS
! DaySim
|-
| 1
| 7,283
| 5,383
| 9,681
|-
| 2
| 59,391
| 80,418
| 84,565
|-
| 3
| 286,647
| 400,012
| 420,057
|-
| 4
| 18,556
| 17,424
| 32,801
|-
| 5
| 13,779
| 10,901
| 20,362
|-
| 6
| 64,325
| 79,338
| 97,402
|- style="font-weight:bold;"
| TOTAL
| 449,980
| 593,476
| 664,868
|}

<h2> Model Calibration and Validation </h2>

The 2017 north florida travel survey and the 2016 onboard rider demographic survey were used to calibrate the model. Traffic counts and transit boarding counts were used to validate the model outputs. The following tables and figures show the results of the calibrated model. These include trip length frequency distributions, tour and trip mode choice model results, highway and transit validation etc.

<h4> Table: Population and Employment </h4>

{| class="wikitable"
|-
! County
! 2015 Population
! 2015 Employment
! BEA 2015
! 2030 Population
! 2030 Employment
! 2045 Population
! 2045 Employment
|-
| Baker
| 23,138
| 9,649
| 9,695
| 32,340
| 15,876
| 37,723
| 20,123
|-
| Clay
| 189,600
| 68,871
| 69,317
| 263,882
| 120,166
| 321,984
| 156,073
|-
| Duval
| 854,757
| 624,952
| 623,596
| 1,078,136
| 786,052
| 1,231,564
| 888,333
|-
| Nassau
| 76,672
| 30,948
| 31,086
| 97,500
| 49,367
| 116,024
| 60,801
|-
| Putnam
| 71,687
| 22,605
| 22,780
| 78,328
| 34,695
| 84,790
| 38,102
|-
| St Johns
| 202,375
| 105,077
| 104,983
| 318,041
| 197,421
| 412,811
| 287,415
|-
| Grand Total
| 1,418,229
| 862,102
| 861,457
| 1,868,227
| 1,203,577
| 2,204,896
| 1,450,847
|}

<h4>Trip Length Frequency Distribution </h4>
[[File:Wsloc.JPG]]

<h4>Tour Mode Choice </h4>
[[File:Tourmode.JPG]]

<h4>Trip Mode Choice </h4>
[[File:Tripmode.JPG]]

<h4>Highway Validation </h4>
[[File:Hwyvalidation.JPG]]

<h4>Transit Validation </h4>
[[File:Trnvalidation.JPG]]

<h2>Software Requirements</h2>

PopulationSim requires the following software, already included in the model setup, and can be found in the following location w.r.t the project folder:

*Anaconda2 with Python 2.7 – “.\User.prg\Population_Synthesis\Anaconda2”

<h2>Setup and Run PopulationSim</h2>

<h3>Setup Directory</h3>

Figure below presents the directory structure for the PopulationSim setup. To set up a PopulationSim run, the user must create the directory structure as shown in below.

<h4>PopulationSim Directory Structure</h4>
[[File:PopSimDir.JPG]]

The folders and files in the directory are explained as follows:

*The data directory holds all the input data like seeds data, control data and crosswalks.
**SCOUNTY Control totals
**TAZ Control totals
**MAZ Control totals
**Geographic Crosswalk
**Household Seed table
**Person Seed table

*The Anaconda2 directory houses the core PopulationSim software files and associated libraries and packages.

*The configs folder contains the settings.yaml file and controls.csv file
**PopulationSim is configured using the settings.yaml file. For this project, it is configured to run in base mode which means it is run from beginning to end and produces a new synthetic population
**controls.csv file specifies all the targets, geography, seed table, control field and their expression to the seed table required for the PopulationSim run.

*The output folder will have the final synthetic household and person file and summary attributes after a successful run.

*This batch file activates the PopulationSim environment and then calls the run_populationsim.py Python script to launch a PopulationSim run.

*PopulationSim is run using the RunPopulationSim.bat batch file in Command Prompt Window. Before starting the run, the path to the Anaconda install must be updated within this file.

The PopulationSim procedure is automated and once you have all the data in place, the user just needs to run the batch file from the command prompt window. To run the PopulationSim for 2030 or 2045 the user need to update the ‘data_dir’ field in the settings.yaml file. The ‘data_dir’ field for different scenario years are:

*2015: The ‘data_dir’ in settings.yaml file should be set to ‘data’

*2030: The ‘data_dir’ in settings.yaml file should be set to ‘data_2030’

*2045: The ‘data_dir’ in settings.yaml file should be set to ‘data_2045’

<h2>Preparing DaySim Inputs</h2>

<h3>Transit Files</h3>

The transit stops file is created using the input transit network from Jacksonville Transit Authority: TROUTE_{Year}{Alt}.LIN
Follow the sequential step below:

*Create a geodatabase (gdb) in Cube

*Import the input transit network file and the highway network file

*Export the nodes (PTNetwork_PTNode) to a shapefile

*Open the shapefile in ArcMap and select the nodes that have STOPNODE=1. These are the transit stops

*Export the selected nodes (stops) to a new stops shapefile. Also Export the transit line file to a shapefile.

*Open the attribute table of the new stops file and add four fields: xcoord_p (double), ycoord_p (double), mode (int) and id (long).

*Calculate xcoord_p and ycoord_p using calculate geometry for the two fields.

*Join transit line by object id and calculate a new field “mode” equal to the MODE field in the line file (1-localbus, 2-skyway). Jacksonville mode codes and daysim mode codes are different, convert these to daysim mode codes before running buffer tools.

{| class="wikitable" style="vertical-align:middle;"
|- style="font-weight:bold; text-align:center;"
! 2015
! 2030
! 2045
! DaySim
|-
| 21 = LB
| 21 = LB
| 21 = LB
| 1 = LB
|-
| 24 = Skyway (coded as LB)
| 23 = Skyway (coded as LB)
| 23 = Skyway (coded as LB)
| 2 = EB
|-
|
| 27 = CR
| 27 = CR
| 3 = CR
|}

*Calculate stopid as “FID+1”.

*Keep only four fields in the attribute table: id, mode, x_coord, y_coord.

*Export all records in the attribute table to a csv file: nftpo_transitstops.csv

<h2>DaySim Data Editing</h2>

With the base year moving from 2010 to 2015, microzones replacing parcels and new population data, the inputs for DaySim needed to be updated. The following steps describe the DaySim input data preparation and update for the NERPM-AB model including the tool and input data used and output formed.

<h3>Network Data Preparation</h3>

This step calculates “nearby” node pairs of microzones for shortest distance path calculations to be used in DaySim.

Tool: '''Network_DataPrepv2.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*input_node.csv (Node x,ys from an all-streets network)
*jax_MAZs_2015.dat (The coordinates of the newly developed microzones)
*jax_netprep.ctl (Network prep control file)

Output:
*input_od_pairs.csv (for input to shortest path update tool)

<h3>Shortest Path Update</h3>

This process is required to generate more accurate short distances based on an all streets network. DTALite, a dynamic traffic assignment software, is used to generate node-to-node shortest path distances using the all streets network.

Tool: '''DTALite64.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*input_od_pairs.csv (from the Network Data Preparation tool)
*input_node.csv (from all-street network)
*input_link_type.csv (from all-street network)
*input_link.csv (from all-street network)
*DTASettings.ini (settings file)

Output:
*output_shortest_path.txt (for input to Buffering microzones)

<h3>Buffering micro-zones</h3>

The Daysim buffering tool is run to prepare MAZ input file for DaySim. This step calculates the new Microzone buffer measures to be used in DaySim.

Tool: '''DSBuffTool.exe'''

Directory: \User.prg\DaySim_Data_Tools

Inputs:
*Jax_microzones_2015.csv (Base Microzone file)
*Jacksonville_Intersections.csv (Street intersections file)
*Jacksonville_transitstops.csv (Transit stops file)
*Jacksonville_openspaces.csv (Open spaces/parks file)
*input_node.csv (All-street Network nodes file)
*output_shortest_path.txt (Node-to-node shortest path distance file)

Output:
*buffered_microzone_2015.dat (to be used in DaySim)
*microzonenode.dat (to be used in DaySim)
*output_shortest_path.txt.bin (Change extension using batch file)
*output_shortest_path.txt.index (Change extension using batch file)

The file extension of the last two outputs above need to be changed for reading into DaySim. The last three output files are used in DaySim to estimate short-distances for car, walk and bike trips.

All three DaySim Data tools can be run using RunAll.bat from the command prompt window.

<h3>Running DSBuffTool.exe</h3>

Double-clicking the executable will bring up a GUI as shown in the following figure. The use of this tool is straightforward. The user just needs to specify all the inputs and click on the “Run” button at the bottom of the GUI.

<h4>DaySim Buffering Tool GUI</h4>
[[File:DBTG.JPG]]

In the “INPUT” section of the GUI, distance calculation and buffer type are set to “Euclidean” and “Logistic decay” respectively. These are the recommended settings for running this tool. If node-to-node distances obtained from an all streets network are available, distance calculation may be set to “Node-to-Node”. The default recommended parameters for logistic decay weights (described in the model design chapter) such as buffer decay slope, offset etc., are also automatically populated in the appropriate fields in the GUI.

All other inputs are to specify file paths that can be done by clicking on the “Browse” button. This pops a file dialog as show in the figure below. The user may also enter full file paths manually using keyboard.

<h4>Buffer Tool File Selection Dialog</h4>
[[File:BTFSD.JPG]]

All file specification fields for the tool are described below. Details about formats for the input files can be found in the directory and data structures chapter.

*MAZ Data File: This is the base MAZ file. It is obtained by running the disaggregation tool.

*Intersection Data File: This file has the coordinates of intersections and the number of links intersecting at each one of them.

*Transit Stops File: This file has the transit stop location coordinates by transit sub-mode.

*Open Spaces File: This file has the locations and area of parks/open spaces in the model region.

*Circuity Data File: This is only required for “Circuity” distance calculation that is now an obsolete method.

When “Node-to-Node” distance calculation is selected, the user need to select a format of the input node-node distance file: output of DTALite or input file used in DaySim. The following files are only needed for “Node-to-Node” distance calculation method.

*Node Data File: This file contains the coordinates of node from an all streets network.

*Node to Node Distance File: This file contains the network shortest path distances between a list of node pairs that are within 3 miles of each other. It is either an output from DTALite or a distance file input to DaySim.

*MAZ-Node Correspondence File: This is an intermediate file created during the buffering process and contains the correspondence between MAZs and nodes.

*Open spaces-Node Correspondence File: This is an intermediate file created during the buffering process and contains the correspondence between open spaces and nodes.

Specification of the following XML files is optional. However, they store the input configuration of a buffering tool run and make it convenient to use the tool.

*Output XML File: If a file is specified in this field, an XML file containing all the input information specified by the user is stored upon clicking on “Run” button that initiated the buffer process.

*XML Input File: Once an XML file has been stored as described above, it can be selected to automatically populate all the input fields required for a buffer run. This can be used to re-run a buffer process with the same inputs or modify a few inputs to generate a different buffered MAZ file.

Finally, Buffered Output File field is used to specify the location and name of the buffered microzone output file that needs to go into DaySim inputs folder.

<h3>DaySim Files Update</h3>

The following files were updated in the latest 2015 DaySim run:

*New Microzone file containing aggregated household and employment inputs and buffered measures instead of parcels

*Household file containing all household information with respect to the microzones

*DaySim Configuration file containing the DaySim execution settings

*A set of updated DaySim input files

*A set of new Data files for short distance calculation

To accurately calculate short car, walk and bike trips, short distance node-to-node measure is introduced in DaySim. This includes the following changes:

*Change in DaySim Configuration file

*Utilization of three additional files in DaySim:
**Microzone node file (microzonenode.dat)
**Node Index file (output_shortest_path_txt_index.dat)
**Node distance file (output_shortest_path_txt_bin.dat)

<h3>Activating TAZ</h3>

The model contains a list of TAZs that are dummy TAZs that act as a placeholder to be later used when building new TAZs. The steps to utilize or activate these dummy zones is as follow:

*Identify the dummy TAZ that needs to be activated.

*Update the input network MicroCodedHnet42.net within Cube.
**Relocate the dummy TAZ to the centroid of new TAZ.
**Update the attributes of the dummy TAZ.
**Build network links that would connect the dummy TAZ to appropriate nodes on the network.
**Update the attributes of these network links.

*Identify the overlap/association between existing MAZs and the new TAZ.

*Update the land-use file nftpo_microzones_year.csv.
**Update the “taz_p” field for MAZs that are now identified as associated with new TAZ for all scenario years.

*Update the PopulationSim Inputs.
**Update the fields associated with MAZs identified in the earlier step with updated TAZ in geo_cross_walk.csv.
**Update the TAZ field for identified MAZs in control_totals_maz.csv file.
**Update the control_totals_taz.csv file by adding a row for dummy TAZ and recording appropriate values in rest of the fields. It should be ensured that the values entered are reasonable for e.g. household size values should not all be 0 and should match up to total households.
**Go through the steps described in Updating Land-use Manually later in this document.

*Update scenario input files in “scenario/Input/” folder.
**Add/Update the “Zone_id” and “Zone_ordinal” field in the _jax_taz_indexes.dat file located in “scenario/Input/DaySimInput/01_TAZ_Index/” folder to the dummy TAZ. Set the “Dest_eligible” field for this TAZ to a value of 1.
**Add/Update the TAZ field in the emp_year.dbf file located in “scenario/Input/DaySimInput/02_Parcel/” folder.
**Add/Update the TAZ field in the _jax_worker_ixxifractions.dat file located in “scenario/Input/DaySimInput/05_ixxi/” folder.
**Add/Update the TAZ field in the county_districts.csv file located in the “scenario/Input/DaySimInput/08_Summaries/” folder.
**Add/Update the TAZ field in the RIVERCROSS.csv file located in the “scenario/Input/” folder.

This concludes the steps needed to activate a dummy TAZ, and the use can now run the regular model.

<h3>Updating Land-use Manually</h3>

When updating land-use data, like household or population attributes, for the base or future year, the steps below should be followed to ensure proper propagation of changes throughout the model:

*The starting point for such changes is the buffering micro-zones step. One of the inputs to the buffering tool is the MAZ file, for example, nftpo_microzones_2015.csv for the 2015 scenario. The user should manually update the land-use file for changes required in specific zones because of new development, changed land-use, etc. The user should then run the buffer tool and copy the output file (buffered_maz_2015.dat) to the scenario input directory.
*The next step is to update the PopulationSim controls by following the guidelines in Section 3.1 for the base year or in Appendix A (Updating Control Data). The user should then run PopulationSim.

*The user should then run the “popsim_to_daysim.bat” file by double clicking it in Windows or running it from a DOS command promt. The batch file runs the “convert.py” script that converts PopulationSim output to DaySim inputs. The DaySim inputs are output in the “./User.prg/PopulationSim/popsim_to_daysim/output/” folder. The output should be renamed as shown in Table 78. These outputs should be copied to the model scenario input directory.

<h4>Table: Rename popsim_to_daysim.bat output</h4>

{| class="wikitable" style="font-weight:bold; text-align:center; vertical-align:middle;"
|-
! rowspan="2" | Output File
! colspan="3" | Rename File
|-
| Base2015
| INT2030
| CF2045
|- style="font-weight:normal; text-align:left;"
| household_2015.dat
| household_2015.dat
| household_2030.dat
| household_2045.dat
|- style="font-weight:normal; text-align:left;"
| person_2015.dat
| person_2015.dat
| person_2030.dat
| person_2045.dat
|}

This concludes the land-use updates step, and the user can now run the regular model.

File:June2020 Training NERPM-AB 2.0.pdf

2020-11-09T15:41:31Z

Bstabler: Bstabler uploaded a new version of File:June2020 Training NERPM-AB 2.0.pdf

File:Figure 6-2.png

2020-11-09T15:34:54Z

Bstabler: Bstabler uploaded a new version of File:Figure 6-2.png

File:Figure 6-1.png

2020-11-09T15:27:13Z

Bstabler: Bstabler uploaded a new version of File:Figure 6-1.png

6.2 Existing Scenarios

2020-11-09T15:10:24Z

Bstabler:

[[Category:6.0 Configuring a Scenario]]

<p>In Cube a scenario defines a value for each key in the catalog. NERPM-AB come with 3 predefined scenarios, Base2015, INT2030 and CF2045. These 3 scenarios can be used as-is to get results for these standard years. If a different scenario needs to be tested, then a new scenario should be created as detailed in section 6.3. If this new scenario involves changes to the roadway network then the steps in <em><strong>Create a Master Network Alternative</strong> </em>to create an alternative should be followed. If the new scenario includes changes to demographic (population or employment) inputs then the steps in section 5.1.2 can be used to edit the parcel file and produce updated DaySim inputs. Finally, if the scenario is for a new year other than 2015, 2030, or 2045 then a new network alternative will need to be created (<em><strong>Create a Master Network Alternative</strong></em>) along with new DaySim inputs (section 5.1.2 Generate default files)</p>

6.1 General Overview

2020-11-09T15:09:04Z

Bstabler: Updated to NERPMAB2

<p><strong><em>Network Change</em></strong></p><p class="BodyParagraph">There are four networks officially part of the LRTP
“2040 Path Forward” effort. These are for the years 2015, 2018 (E+C), 2030 and
2045. Table 6.1 provides the user with information regarding the period of
construction related to the planned roadway projects within the North Florida
TPO’s area.</p><p></p><p><strong>Table 6-1 Path Forward 2040 Cost Feasible Plan Project List</strong></p><div class="table-wrap">
{| class="wikitable"
! class="confluenceTh" | County
! class="confluenceTh" | Map Id
! class="confluenceTh" | Facility
! class="confluenceTh" | From
! class="confluenceTh" | To
! class="confluenceTh" | Improvement Type
! class="confluenceTh" | Construction Year Band
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Clay
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
1027
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | CR
218
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Cosmos
Avenue
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Pine
Tree Lane
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 4 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Clay
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
33
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | CR 220
Doctors Inlet Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 21
Blanding Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Knight
Boxx Road (end of four lane)
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 4 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Clay
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
114
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 21
Blanding Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR
16
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | CR 215
Blanding Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 4 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Clay
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
41
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at CR
218
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Clay
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
1012
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at CR
739
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Clay
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
1013
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at SR
16
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Clay
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
8202
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US
17
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 21
Blanding Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New 4
Lane Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Clay
| class="highlight-green confluenceTd" data-highlight-colour="green" | 34
| class="highlight-green confluenceTd" data-highlight-colour="green" | CR 220
Doctors Inlet Road
| class="highlight-green confluenceTd" data-highlight-colour="green" | College
Drive
| class="highlight-green confluenceTd" data-highlight-colour="green" | US
17
| class="highlight-green confluenceTd" data-highlight-colour="green" | Widen to
6 Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Clay
| class="highlight-green confluenceTd" data-highlight-colour="green" | 106
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR
16
| class="highlight-green confluenceTd" data-highlight-colour="green" | CR
218
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 15A
Oakridge Avenue
| class="highlight-green confluenceTd" data-highlight-colour="green" | Widen to
4 Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Clay
| class="highlight-green confluenceTd" data-highlight-colour="green" | 908
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR
16
| class="highlight-green confluenceTd" data-highlight-colour="green" | US
17
| class="highlight-green confluenceTd" data-highlight-colour="green" | Shands
Bridge
| class="highlight-green confluenceTd" data-highlight-colour="green" | Widen to
4 Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Clay
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 13
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Cheswick
Oak Avenue Extension
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Oakleaf
Plantation Parkway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Savannah
Glen Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New 4 Lane
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Clay
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 1026
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | CR
218
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US
301
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Cosmos
Avenue
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 4
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Clay/St.
John's
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 8203
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 23
First Coast Expressway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US 17
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New 6 Lane
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2021-2025
|-
! class="confluenceTh" | County
! class="confluenceTh" | Map Id
! class="confluenceTh" | Facility
! class="confluenceTh" | From
! class="confluenceTh" | To
! class="confluenceTh" | Improvement Type
! class="confluenceTh" | Construction Year Band
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1019
| class="highlight-red confluenceTd" data-highlight-colour="red" | 5th Street
(McDuff Avenue Phase 3)
| class="highlight-red confluenceTd" data-highlight-colour="red" | Melson
Avenue
| class="highlight-red confluenceTd" data-highlight-colour="red" | Huron
Street
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 3
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 2
| class="highlight-red confluenceTd" data-highlight-colour="red" | Alta
Drive
| class="highlight-red confluenceTd" data-highlight-colour="red" | Faye
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Burkit
Lane
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 5
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 15
| class="highlight-red confluenceTd" data-highlight-colour="red" | Collins
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 21
Blanding Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | Pine
Verde
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 4
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 930
| class="highlight-red confluenceTd" data-highlight-colour="red" | Collins
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Shindler
Drive
| class="highlight-red confluenceTd" data-highlight-colour="red" | Rampart
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 4
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 932
| class="highlight-red confluenceTd" data-highlight-colour="red" | Collins
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Old
Middleburg Road S
| class="highlight-red confluenceTd" data-highlight-colour="red" | Shindler
Drive
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 4
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 37
| class="highlight-red confluenceTd" data-highlight-colour="red" | Eastport
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 105
Heckscher Drive
| class="highlight-red confluenceTd" data-highlight-colour="red" | Pulaski
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 3
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1021
| class="highlight-red confluenceTd" data-highlight-colour="red" | Florida
Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | Penman
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 10
Atlantic Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 3
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 49
| class="highlight-red confluenceTd" data-highlight-colour="red" | Hartley
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 13 San
Jose Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | Old St.
Augustine Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 3
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1022
| class="highlight-red confluenceTd" data-highlight-colour="red" | I-295
| class="highlight-red confluenceTd" data-highlight-colour="red" | I-95
South
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 13 San
Jose Boulevard (Buckman Bridge)
| class="highlight-red confluenceTd" data-highlight-colour="red" | Add 2
Express Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1023
| class="highlight-red confluenceTd" data-highlight-colour="red" | I-295
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 202 J. T.
Butler Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 9B
| class="highlight-red confluenceTd" data-highlight-colour="red" | Add 2
Express Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 73
| class="highlight-red confluenceTd" data-highlight-colour="red" | Kernan
Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 202 J. T.
Butler Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | Glen Kernan
Parkway
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 6
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 933
| class="highlight-red confluenceTd" data-highlight-colour="red" | Kernan
Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 10
Atlantic Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | McCormick
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 6
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1015
| class="highlight-red confluenceTd" data-highlight-colour="red" | Old
Middleburg Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Argyle
Forest Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 134 103rd
Street
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 4
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 953
| class="highlight-red confluenceTd" data-highlight-colour="red" | Pulaski
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Eastport
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | I-295
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 4
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 935
| class="highlight-red confluenceTd" data-highlight-colour="red" | Ramona
Boulevard (Crystal Springs Road Phase 2)
| class="highlight-red confluenceTd" data-highlight-colour="red" | Hammond
Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | Cahoon
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 5
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 934
| class="highlight-red confluenceTd" data-highlight-colour="red" | Ricker
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Morse
Avenue
| class="highlight-red confluenceTd" data-highlight-colour="red" | Old
Middleburg Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 3
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1009
| class="highlight-red confluenceTd" data-highlight-colour="red" | San Pablo
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | US 90 Beach
Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 10
Atlantic Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 3
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 101
| class="highlight-red confluenceTd" data-highlight-colour="red" | Shindler
Drive
| class="highlight-red confluenceTd" data-highlight-colour="red" | Collins
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 134 103rd
Street
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 3
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 960
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 115
Southside Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | Gate
Parkway
| class="highlight-red confluenceTd" data-highlight-colour="red" | Hogan
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Intersection
Improvements
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 957A
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 21
Blanding Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | at Collins
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" |
| class="highlight-red confluenceTd" data-highlight-colour="red" | Intersection
Improvements
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 957B
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 21
Blanding Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | at SR 134
103rd Street
| class="highlight-red confluenceTd" data-highlight-colour="red" |
| class="highlight-red confluenceTd" data-highlight-colour="red" | Intersection
Improvements
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 957C
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 21
Blanding Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" | at Cedar
Hills Boulevard
| class="highlight-red confluenceTd" data-highlight-colour="red" |
| class="highlight-red confluenceTd" data-highlight-colour="red" | Intersection
Improvements
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 701
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 243 JIA
North Access Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 102
Airport Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Pecan Park
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 4
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Duval
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1024
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 9B
| class="highlight-red confluenceTd" data-highlight-colour="red" | Phillips
Highway (US 1)
| class="highlight-red confluenceTd" data-highlight-colour="red" | I-295
| class="highlight-red confluenceTd" data-highlight-colour="red" | Add 2
Auxiliary Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
1016
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Cahoon
Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Lenox
Avenue
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US 90
Beaver Street
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Reconstruct 2 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
972
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Cecil
Field Connector
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Commerce Center
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New 2
Lane Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
971
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Chaffee Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 228
Normandy Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Crystal Springs Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 5 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
1020
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Clark
Road Extension
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Broward Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Clark
Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New 3
Lane Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
121
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
I-295
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 113
Southside Connector
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 202
J. T. Butler Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Add 4
Express Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
964
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
I-295
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at US
17 Roosevelt Boulevard to Wells Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Modify
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
68
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
I-95
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at
Airport Road (SR 102) - Phase 2
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Modify
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
418A
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
I-95
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval/St. John's County Line
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
I-295
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Add 4
Express Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
714
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 10
Atlantic Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at
Girvin Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Intersection Improvements
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
715
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 10
Atlantic Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at
Hodges Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Intersection Improvements
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
716
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 10
Atlantic Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at San
Pablo Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Intersection Improvements
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
968
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 115
Southside Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at SR
10 Atlantic Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Intersection Improvements
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | 9
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 21
Blanding Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
I-295
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Wilson
Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 6 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
701
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 243
JIA North Access Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 102
Airport Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Pecan
Park Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 4 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
959
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US 17
Main Street
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at
Eastport Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
1017
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US 17
Main Street
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Berlin Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Pecan
Park Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 4 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
16
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US 17
Roosevelt Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at
Collins Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Duval
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | 7
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US 90
Beaver Street
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Cahoon
Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | McDuff
Avenue
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 4 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 1
| class="highlight-green confluenceTd" data-highlight-colour="green" | Alta
Drive Realignment
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 105
Zoo Parkway
| class="highlight-green confluenceTd" data-highlight-colour="green" | North of
New Berlin Road (south)
| class="highlight-green confluenceTd" data-highlight-colour="green" | New 4
Lane Road
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 406
| class="highlight-green confluenceTd" data-highlight-colour="green" |
I-10
| class="highlight-green confluenceTd" data-highlight-colour="green" | US
301
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 23
First Coast Expressway
| class="highlight-green confluenceTd" data-highlight-colour="green" | Add 4
Express Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 57
| class="highlight-green confluenceTd" data-highlight-colour="green" |
I-295
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 13
San Jose Boulevard
| class="highlight-green confluenceTd" data-highlight-colour="green" | W of US
17 Roosevelt Boulevard (Buckman Bridge)
| class="highlight-green confluenceTd" data-highlight-colour="green" | Add 4
Express Lanes on Bridge
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 410
| class="highlight-green confluenceTd" data-highlight-colour="green" |
I-295
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 134
103rd Street
| class="highlight-green confluenceTd" data-highlight-colour="green" |
I-10
| class="highlight-green confluenceTd" data-highlight-colour="green" | Add 4
Express Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 419
| class="highlight-green confluenceTd" data-highlight-colour="green" |
I-95
| class="highlight-green confluenceTd" data-highlight-colour="green" | at US
1/SR 15 MLK
| class="highlight-green confluenceTd" data-highlight-colour="green" |
| class="highlight-green confluenceTd" data-highlight-colour="green" | Modify
Interchange
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 962
| class="highlight-green confluenceTd" data-highlight-colour="green" |
I-95
| class="highlight-green confluenceTd" data-highlight-colour="green" | at SR
115 Southside Boulevard
| class="highlight-green confluenceTd" data-highlight-colour="green" |
| class="highlight-green confluenceTd" data-highlight-colour="green" | Modify
Interchange
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" |
1034
| class="highlight-green confluenceTd" data-highlight-colour="green" | New
World Avenue
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 23
First Coast Expressway
| class="highlight-green confluenceTd" data-highlight-colour="green" | Chaffee
Road
| class="highlight-green confluenceTd" data-highlight-colour="green" | New 2
Lane Road
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 940
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 104
Dunn Avenue
| class="highlight-green confluenceTd" data-highlight-colour="green" | US 1 SR
15 New Kings Road
| class="highlight-green confluenceTd" data-highlight-colour="green" |
I-295
| class="highlight-green confluenceTd" data-highlight-colour="green" | Widen to
4 Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 941
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 104
Dunn Avenue
| class="highlight-green confluenceTd" data-highlight-colour="green" | Old
Kings Road
| class="highlight-green confluenceTd" data-highlight-colour="green" | US 23
New Kings Road
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Intersection Improvements
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 102
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 115
Southside Boulevard
| class="highlight-green confluenceTd" data-highlight-colour="green" | US 1/SR
5 Philips Highway
| class="highlight-green confluenceTd" data-highlight-colour="green" | I-95
Ramps
| class="highlight-green confluenceTd" data-highlight-colour="green" | Widen to
6 Lanes*
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Duval
| class="highlight-green confluenceTd" data-highlight-colour="green" | 955
| class="highlight-green confluenceTd" data-highlight-colour="green" | Trout
River Boulevard
| class="highlight-green confluenceTd" data-highlight-colour="green" | Old
Kings Road
| class="highlight-green confluenceTd" data-highlight-colour="green" | US 23
New Kings Road
| class="highlight-green confluenceTd" data-highlight-colour="green" | Widen to
4 Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 2
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Alta Drive
/ Yellow Bluff Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New Berlin
Road (north)
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 4
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 432
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-10
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 454
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-10
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | at
I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Modify
Interchange
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 403
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-10
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau/Duval County Line
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US
301
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 407
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95 North
Interchange
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Dames
Point Bridge/ SR 105 Heckscher Drive
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 408
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | North of
Trout River
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95 North
Interchange
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 409
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | South of
US 1 New Kings Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | North of
Trout River
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 411
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-10
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | South of
US 1 New Kings Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 431
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US 17
Roosevelt Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 134
103rd Street
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 433
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 202 J.
T. Butler Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 10
Atlantic Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 434
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 202 J.
T. Butler Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 1033
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | National
Cemetery Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Lannie
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Arnold
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New 2 Lane
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 948
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New Berlin
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Pulaski
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Yellow
Bluff Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 4
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 949
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New Berlin
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Yellow
Bluff Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Cedar
Point Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 4
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 103
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 115
Southside Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 202 J.
T. Butler Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US 90
Beach Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 6
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 966
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 115
Southside Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | at SR 202
J. T. Butler Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Modify
Interchange
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 151
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR
A1A
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 116
Wonderwood Drive
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Naval
Station Mayport North Gate
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 4
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 94
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US 1 SR 5
Philips Highway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 9B
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-295
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 6
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Duval
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 95
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US 1 SR 5
Philips Highway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95 at
the Avenues
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 202 J.
T. Butler Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 6
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
! class="confluenceTh" | County
! class="confluenceTh" | Map Id
! class="confluenceTh" | Facility
! class="confluenceTh" | From
! class="confluenceTh" | To
! class="confluenceTh" | Improvement Type
! class="confluenceTh" | Construction Year Band
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | Nassau
| class="highlight-red confluenceTd" data-highlight-colour="red" | 708
| class="highlight-red confluenceTd" data-highlight-colour="red" | US 301 SR
200
| class="highlight-red confluenceTd" data-highlight-colour="red" | US 1 S Kings
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" |
| class="highlight-red confluenceTd" data-highlight-colour="red" | Intersection
Improvements
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Nassau
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
708
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US 301
SR 200
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US 1 S
Kings Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Intersection Improvements
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Nassau
| class="highlight-green confluenceTd" data-highlight-colour="green" | 703
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 200
SR A1A
| class="highlight-green confluenceTd" data-highlight-colour="green" | Amelia
Island Parkway
| class="highlight-green confluenceTd" data-highlight-colour="green" | Sadler
Road
| class="highlight-green confluenceTd" data-highlight-colour="green" | Widen to
6 Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Nassau
| class="highlight-green confluenceTd" data-highlight-colour="green" | 707
| class="highlight-green confluenceTd" data-highlight-colour="green" | US
17
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Nassau/Duval County Line
| class="highlight-green confluenceTd" data-highlight-colour="green" | Harts
Road
| class="highlight-green confluenceTd" data-highlight-colour="green" |
Intersection Improvements
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 704
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Amelia
Island Parkway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | South 14th
Street
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | CR 105A
Buccaneer Trail
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 4
Lanes*
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 501
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Concourse
Loop Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | License
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Amelia
Concourse
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New 2 Lane
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 705
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | CR 105A
Buccaneer Trail
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | South
Fletcher Avenue
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Canopy
Drive
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Turn Lane
Improvements*
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 402
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-10
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Baker/Nassau County Line
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau/Duval County Line
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Add 4
Express Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 981
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR
200
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Fernandina
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Intersection Improvements
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 706
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR
A1A
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | South
Fletcher Avenue
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Amelia
Island Parkway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Turn Lane
Improvements
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 427
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR A1A SR
200
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | at US
17
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Intersection Improvements
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Nassau
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 708
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US 301 SR
200
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US 1 S
Kings Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
Intersection Improvements
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
! class="confluenceTh" | County
! class="confluenceTh" | Map Id
! class="confluenceTh" | Facility
! class="confluenceTh" | From
! class="confluenceTh" | To
! class="confluenceTh" | Improvement Type
! class="confluenceTh" | Construction Year Band
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | St.
Johns
| class="highlight-red confluenceTd" data-highlight-colour="red" | 810
| class="highlight-red confluenceTd" data-highlight-colour="red" | CR 2209
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 23 First
Coast Expressway
| class="highlight-red confluenceTd" data-highlight-colour="red" | CR 210
| class="highlight-red confluenceTd" data-highlight-colour="red" | New 6 Lane
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | St.
Johns
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1025
| class="highlight-red confluenceTd" data-highlight-colour="red" | Dixie
Highway / Pellicer Lane
| class="highlight-red confluenceTd" data-highlight-colour="red" | CR 214 King
Street
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 207
| class="highlight-red confluenceTd" data-highlight-colour="red" | Reconstruct
2 Lanes and Widen to add Sidewalks and Bike Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | St.
Johns
| class="highlight-red confluenceTd" data-highlight-colour="red" | 835
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 16
| class="highlight-red confluenceTd" data-highlight-colour="red" |
International Golf Parkway
| class="highlight-red confluenceTd" data-highlight-colour="red" | South
Francis Road
| class="highlight-red confluenceTd" data-highlight-colour="red" | Widen to 4
Lanes
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | St.
Johns
| class="highlight-red confluenceTd" data-highlight-colour="red" | 831
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 313
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 207
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR 16
| class="highlight-red confluenceTd" data-highlight-colour="red" | New 6 Lane
Road
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-red confluenceTd" data-highlight-colour="red" | St.
Johns
| class="highlight-red confluenceTd" data-highlight-colour="red" | 1008
| class="highlight-red confluenceTd" data-highlight-colour="red" | SR A1A
| class="highlight-red confluenceTd" data-highlight-colour="red" | at Red
Cox/Coquina Rd
| class="highlight-red confluenceTd" data-highlight-colour="red" |
| class="highlight-red confluenceTd" data-highlight-colour="red" | Intersection
Improvements
| class="highlight-red confluenceTd" data-highlight-colour="red" |
2019-2020
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
Johns
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
811
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | CR
2209
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 16
Connector
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New 6
Lane Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
Johns
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
818
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | CR
305
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | CR
204
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR
206
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New 2
Lane Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
Johns
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
418B
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
I-95
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
International Golf Parkway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
John's/Duval County Line
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Add 4
Express Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
Johns
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
828
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Race
Track Road
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Bartram Park Boulevard
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
Bartram Springs
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Widen
to 4 Lanes
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
Johns
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
840
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at
I-95
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
Johns
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
841
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at CR
16 A
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
Johns
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
847
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | SR 23
First Coast Expressway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at CR
2209
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | New
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | St.
Johns
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
845
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | US 1
SR 5 Philips Highway
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | at CR
210
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" | Modify
Interchange
| class="highlight-yellow confluenceTd" data-highlight-colour="yellow" |
2021-2025
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" | St.
Johns
| class="highlight-green confluenceTd" data-highlight-colour="green" | 812
| class="highlight-green confluenceTd" data-highlight-colour="green" | CR
2209
| class="highlight-green confluenceTd" data-highlight-colour="green" |
International Golf Parkway
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR 16
Connector
| class="highlight-green confluenceTd" data-highlight-colour="green" | New 6
Lane Road
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" | St.
Johns
| class="highlight-green confluenceTd" data-highlight-colour="green" | 816
| class="highlight-green confluenceTd" data-highlight-colour="green" | CR
2209
| class="highlight-green confluenceTd" data-highlight-colour="green" | CR
208
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR
16
| class="highlight-green confluenceTd" data-highlight-colour="green" | New 4
Lane Road
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" | St.
Johns
| class="highlight-green confluenceTd" data-highlight-colour="green" | 832
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR
313
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR
16
| class="highlight-green confluenceTd" data-highlight-colour="green" | US 1
Dixie Highway
| class="highlight-green confluenceTd" data-highlight-colour="green" | New 4
Lane Road
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-green confluenceTd" data-highlight-colour="green" | St.
Johns
| class="highlight-green confluenceTd" data-highlight-colour="green" | 800
| class="highlight-green confluenceTd" data-highlight-colour="green" | SR
A1A
| class="highlight-green confluenceTd" data-highlight-colour="green" | Mickler
Road
| class="highlight-green confluenceTd" data-highlight-colour="green" | Palm
Valley Road
| class="highlight-green confluenceTd" data-highlight-colour="green" | Widen to
4 Lanes
| class="highlight-green confluenceTd" data-highlight-colour="green" |
2026-2030
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | St.
Johns
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 813
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | CR
2209
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | CR
214
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | CR
208
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New 4 Lane
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | St.
Johns
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 815
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | CR
2209
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR 16
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
International Golf Parkway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | New 4 Lane
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | St.
Johns
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 827
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Race Track
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | St. Johns
Parkway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Bartram
Park Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 4
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | St.
Johns
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 827
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Race Track
Road
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | St. Johns
Parkway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Bartram
Park Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 4
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | St.
Johns
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 113
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR
207
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | I-95
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | South
Holmes Boulevard
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 6
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | St.
Johns
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | 115
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR
312
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | US 1/SR 5
Dixie Highway
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | SR
A1A
| class="highlight-blue confluenceTd" data-highlight-colour="blue" | Widen to 6
Lanes
| class="highlight-blue confluenceTd" data-highlight-colour="blue" |
2031-2040
|-

|}</div><p></p><h4 id="id-6.1GENERALOVERVIEW-EmploymentChange"><em>Employment Change</em></h4><p class="BodyParagraph">In future, some areas may see growth/decline in employment due to increase/decrease in business establishments or some other reasons. In addition, the distribution of different employment types could change over the years.</p><p class="BodyParagraph">In order to reflect those changes in the model, the DaySim buffered parcel file needs to be updated - \Master\{SCENARIO_DIR}\
Input\DaySimInput\02_Parcel\buffered_maz_year.dat.</p><p class="BodyParagraph">To update the file, two steps should be performed:</p><ul><li>If employment at a TAZ is same, including in different sectors, but distribution of employment in microzones within the TAZ have changed then either of the two should be undertaken:
<ul><li>Edit the land-use file manually.</li><li>Get new block level controls (with employment in 2-digit NAICS categories)
and run the allocation tool (see section 5.1 |) to obtain an updated buffered parcel file.</li></ul></li><li>If employment (or/and distribution of employment sectors) at a TAZ is different, the new TAZ file should be used to run the allocation tool (see section 5.1 |). The tool will produce an updated parcel file.</li><li>The first step would create a new land-use file and an updated TAZ file, if necessary. The new land-use file should be provided as input to the buffering tool (see section 5.1 | )and generate a new buffered parcel file. This file should then be replaced in the DaySimInput folder.</li></ul><h4 id="id-6.1GENERALOVERVIEW-PopulationChange"><em>Population Change</em></h4><p class="BodyParagraph">After a base year population has been synthesized, new synthetic populations may need to be generated based on the type scenario being run or the policy being tested. For example, a change in the network may not require the population to be synthesized again. On the other hand, if a land-use policy variable is being tested which would result in significantly different patterns in the population and employment levels in the TAZs than in the base case, a new synthetic population would be have to be generated. The same would be the case for a future year run when the population mix or distribution of specific population characteristics may deviate from those in the base synthetic population. Population is likely to increase in future, however, it may not increase proportionately geographically. A change in employment may also alter the existing geographic distribution of the population. To accommodate a change in population following three input files need to be updated:</p><ul><li>Buffered parcel file (\Master\{SCENARIO_DIR}\Input\DaySimInput\02_Parcel\
buffered_maz_year.dat)</li><li>Household file
(\Master\{SCENARIO_DIR}\Input\DaySimInput\03_Household\household_year.dat)</li><li>Person file
(\Master\{SCENARIO_DIR}\Input\DaySimInput\04_Person\person_year.dat)</li></ul><p class="BodyParagraph">To update the buffered parcel file, the similar steps
as employment change accommodation needs to be performed. First, the land-use file (output of the allocation tool) is updated and then it is used in the buffering tool to generate a new buffered parcel file that goes into DaySim.</p><p class="BodyParagraph">Synthetic population data (household and person files) would also need updates. As previously described, the synthetic population is
generated using PopulationSim. The marginal control files for population synthesis need to be updated to reflect the new control totals. After that synthetic population needs to be generated using the new input files (Please refer to PopulatinSim guide for synthesis instructions).</p>

5.2 DaySim Input Preparation - Running PopulationSim

2020-11-09T14:57:04Z

Bstabler: Updated to NERPMAB2

[[Category:5.0 User Interface & Running the Model]]

<p class="BodyParagraph">PopulationSim should be run when there are changes in the population in the regions' MAZs and TAZs. For example, if additional households are added to an MAZ, the user should run PopulationSim again to account for that change. The following steps illustrates how to run PopulationSim. The video webinar also demonstrates the process in detail. </p>

<h3> Update the Controls</h3>

The user should update the control files according to the changes in the population. For example, if there are 100 additional households in MAZ 1215, then the user should open the ''nftpo_microzones_2015.csv'' file and add 100 additional households to MAZ 1215. The user should then edit ''update_controls.S'' script to point to correct directories and files to update. The user then should run ''update_controls.bat'' file by double-clicking. The control files should now be updated.

[[File:Step1popsim.JPG]]
[[File:Step11popsim.JPG]]

<h3> Run PopulationSim </h3>

PopulationSim is run from a batch file named ''RunPopulationSim.bat''. Once the user has updated the control files, the user should open a command window in the ''Population_Synthesis'' directory and run the batch file as shown in the figure below.

[[File:Step2popsim.JPG]]

<h3> Convert PopulationSim Outputs to DaySim Inputs </h3>

The primary outputs of PopulationSim are the synthetic_households.csv and the synthetic_persons.csv files. These files need to be reformatted before they can be used by DaySim. There is an python script that performs this conversion. This python script is called within a batch script. The user needs to run this batch script to perform the conversion. The training document demonstrates how to run this script.

[[File:Step3popsim.JPG]]

File:Step3popsim.JPG

2020-11-09T14:54:45Z

Bstabler: Bstabler uploaded a new version of File:Step3popsim.JPG

File:Step11popsim.JPG

2020-11-09T14:50:18Z

Bstabler: Update Control batch script.

Update Control batch script.

4.4 DaySim-Cube Linkage

2020-11-09T14:38:28Z

Bstabler: Updated to NERPMAB2

[[Category:4.0 Directory & Data Structures]]

Cube Voyager scripts produce highway and transit skims in matrix (.MAT) format. The skims are converted to ASCII text files and are input to DaySim, which simulates person trips and outputs them into .tsv list format (_tour.tsv, _trip.tsv etc.). In addition, DaySim also writes out a simple trip list “Tdm_trip_list.csv” in \Master\{SCENARIO_DIR}\Output\DaySim. This file just has origin TAZ, destination TAZ, mode, departure time, arrival time, trip duration, purpose, and VOT category. This trip list is transformed into time-of-day, mode, and purpose specific highway and transit matrices for assignment. The matrices are created under the directory\Master\{SCENARIO_DIR}\Output\DaySim – VEHTRIPS_AM.MAT, VEHTRIPS_MD.MAT, VEHTRIPS_PM.MAT, VEHTRIPS_EV.MAT, and TRANSITTAB_A10.MAT.

4.1 Master Model Directory

2020-11-09T14:35:39Z

Bstabler: Updated to NERPMAB2

[[Category:4.0 Directory & Data Structures]]

The master model directory is called “NERPMABv2.1”. All files related to the model can be found within this directory. The following sub-directories are particularly important:

*Applications – This folder contains all the Cube Voyager scripts and applications
*Doc – Documents related to the model are here
*Input_SWM – All inputs related to the State Wide Model (SWM) are inside this folder. Recall that a sub-area is extracted from SWM to derive port freight trips.
*Output_SWM – this folder contains the outputs from the sub-area extraction process.
*Parameters – a set of inputs files including the input highway network can be found in this folder.
*Master - This folder contains the model runs including inputs for DaySim and all model outputs. Each model scenario is housed in a sub-folder in the Master folder.
*R-3.4.4 - This folder contains the R application that can be used for calibrating and validating DaySim outputs.
*User.prg – this folder contains other user programs that are not a part of the Cube framework. Currently R scripts that are used to create calibration targets for demand model from NHTS live here. In addition, there are also utilities that are used to create various inputs for DaySim activity-based model. These include the DaySim Data Tools - a set of tools used to create input for DaySim. Another tool in this folder is PopulationSim, under the Population Synthesis sub-folder. This tool is used to generate synthetic population for the model.

3.5 Synthetic Population

2020-11-09T14:29:02Z

Bstabler: Updated to NERPMAB2

[[Category:3.0 Model Design]]

<h3 id="id-3.5SYNTHETICPOPULATION-Overview"><span style="color: rgb(255,102,0);">Overview</span></h3><p class="BodyParagraph">In trip-based models, trip rates are applied to aggregate households grouped in Traffic Analysis Zones (TAZs) to generate trips. On the other hand, in an activity-based model (ABM), choices involving activities and trips are simulated for each of the individual persons in households. Hence, it is necessary to first develop a “synthetic population” of the regions’ residents. Synthetic population is a list of households and persons that is based on observed or forecasted distributions of socioeconomic attributes and created by sampling detailed Census microdata. This produces individual household agents and individual person agents that are subjects of the simulation.</p><p class="BodyParagraph">Prior to their use in the simulation, synthetic populations are represented in data tables, often in a relational database or some equivalently structured file system. Typically there are separate tables for households and person records. The household records file provides details about various household-level socio-demographic attributes such as household income, size, number of workers, etc. Similarly, the person records file provides information about person-level attributes such as age, gender, employment status, etc. Person records are linked to household records through ID numbers.</p><h4 class="BodyParagraph" id="id-3.5SYNTHETICPOPULATION-TABLE3-2SAMPLEHOUSEHOLDRECORDSFILE">TABLE 3-2 SAMPLE HOUSEHOLD RECORDS FILE</h4><div class="table-wrap">
{| class="wikitable"
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhno</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhsize</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhvehs</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhwkrs</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhftw</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhptw</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhret</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhoad</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhuni</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhhsc</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hh515</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhcu5</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhincome</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hownrent</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hrestype</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhparcel</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhtaz</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhexpfac</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>samptype</strong></p>
|-
| class="confluenceTd" | <p>1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">75095</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">2699</p>
| class="confluenceTd" | <p align="center">227</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">11</p>
|-
| class="confluenceTd" | <p>2</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">58074</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">2699</p>
| class="confluenceTd" | <p align="center">227</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">11</p>
|-
| class="confluenceTd" | <p>3</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">0</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">30288</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">3</p>
| class="confluenceTd" | <p align="center">23479</p>
| class="confluenceTd" | <p align="center">227</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">11</p>
|-
| class="confluenceTd" | <p>4</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">0</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1802</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">3</p>
| class="confluenceTd" | <p align="center">37105</p>
| class="confluenceTd" | <p align="center">227</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">11</p>
|}</div><h4 id="id-3.5SYNTHETICPOPULATION-TABLE3-3SAMPLEPERSONRECORDSFILE">TABLE 3-3 SAMPLE PERSON RECORDS FILE</h4><div class="table-wrap">
{| class="wikitable"
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>hhno</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pno</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pptyp</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pagey</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pgend</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pwtyp</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pwpcl</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pwtaz</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pwautime</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pwaudist</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pstyp</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pspcl</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pstaz</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>psautime</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>psaudist</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>puwmode</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>puwarrp</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>puwdepp</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>ptpass</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>ppaidprk</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pdiary</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>pproxy</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>psexpfac</strong></p>
|-
| class="confluenceTd" | <p>1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">41</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">0</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
|-
| class="confluenceTd" | <p>2</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">34</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">0</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
|-
| class="confluenceTd" | <p>3</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">5</p>
| class="confluenceTd" | <p align="center">26</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">0</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
|-
| class="confluenceTd" | <p>4</p>
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">4</p>
| class="confluenceTd" | <p align="center">34</p>
| class="confluenceTd" | <p align="center">2</p>
| class="confluenceTd" | <p align="center">0</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">0</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">-1</p>
| class="confluenceTd" | <p align="center">1</p>
|}</div><p class="BodyParagraph">Population synthesized by any synthetic population generator may be used with DaySim as long as the required household and person socio-demographic attributes are provided to it in the appropriate format. PopulationSim is an open platform for population synthesis and survey weighting. It emerged from Oregon DOT’s desire to build a shared, open, platform that could be easily adapted for statewide, regional, and urban transportation planning needs. It has the ability to control for both household and person level demographic attributes simultaneously.</p><h3 id="id-3.5SYNTHETICPOPULATION-PreparingSyntheticPopulationsforDaySim"><span style="color: rgb(255,102,0);">Preparing Synthetic Populations for DaySim</span></h3><p class="BodyParagraph">The design of the synthetic population should support the design of the activity-based model (DaySim in this case) and provide the variables it needs. In addition, the activity-based model should only rely on information that can be realistically provided in the synthetic population.</p><p class="BodyParagraph">Population synthesis generally consists of the synthesis of two sub-populations – those living in regular households and those living in non-institutionalized group quarters such as college dormitories. For this effort, an additional segment of population was synthesized which comprised of seasonal households. These segments were established to reflect the differences in travel patterns associated with these sub-populations as well as to provide the ability to support seasonal analyses. For example, the seasonal population is generally older than the permanent population, has lower levels of workforce participation, and clusters in certain geographic areas. All of these attributes influence travel patterns and the demand for travel.</p><p class="BodyParagraph">There are three major steps in creating a synthetic population:</p><ol><li>Specifying the inputs to the process—the control variables and sample households as well as the level of geographic resolution. Specifying the control variables is essential. In addition, there is often an additional step of specifying additional, uncontrolled variables to be added to the synthetic population.</li><li>Actually running a program that produces the synthetic households.</li><li>The third major step would be transforming the model-generated outputs into characteristics of the population that will be used throughout the rest of the model system. This could involve creating categorical variables out of continuous variables, reformulating income, or allocating households from the zonal level to a finer level of geographic resolution, such as a parcel.</li></ol><h4 id="id-3.5SYNTHETICPOPULATION-DaySimPersonTypes"><em>DaySim Person Types</em></h4><p class="BodyParagraph">Although person are being modeled in disaggregate form in an ABM, it is often useful to create person type categories. DaySim uses 8 such person types. Person type categories may be used for various purposes:</p><ol><li>As a basic segmentation for certain models, such as daily activity pattern models</li><li>To summarize and compare observed versus estimated data and calibrate models</li><li>As explanatory variables in models</li><li>As constraints on alternatives that are available; for example, work and school activities are only available to workers and student; and driving is restricted by age</li></ol><h4 id="id-3.5SYNTHETICPOPULATION-TABLE3-4DAYSIMPERSONTYPES">TABLE 3-4 DAYSIM PERSON TYPES</h4><div class="table-wrap">
{| class="wikitable"
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong> No.</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>Person Type</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>Age</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>Work Status</strong></p>
| class="highlight-red confluenceTd" data-highlight-colour="red" | <p class="TableHeadingGray"><strong>School Status</strong></p>
|-
| class="confluenceTd" | <p align="center">1</p>
| class="confluenceTd" | <p align="center">Full-time worker</p>
| class="confluenceTd" | <p align="center">18 or more</p>
| class="confluenceTd" | <p align="center">Full-time</p>
| class="confluenceTd" | <p align="center">None/Part-time</p>
|-
| class="confluenceTd" | <p align="center">2</p>
| class="confluenceTd" | <p align="center">Part-time worker</p>
| class="confluenceTd" | <p align="center">18 or more</p>
| class="confluenceTd" | <p align="center">Part-time</p>
| class="confluenceTd" | <p align="center">None/Part-time</p>
|-
| class="confluenceTd" | <p align="center">3</p>
| class="confluenceTd" | <p align="center">Retired person</p>
| class="confluenceTd" | <p align="center">65 or more</p>
| class="confluenceTd" | <p align="center">Unemployed</p>
| class="confluenceTd" | <p align="center"></p>
|-
| class="confluenceTd" | <p align="center">4</p>
| class="confluenceTd" | <p align="center">Non-working adult</p>
| class="confluenceTd" | <p align="center">Less than 65</p>
| class="confluenceTd" | <p align="center">Unemployed</p>
| class="confluenceTd" | <p align="center">None/Part-time</p>
|-
| class="confluenceTd" | <p align="center">5</p>
| class="confluenceTd" | <p align="center">University student</p>
| class="confluenceTd" | <p align="center">18 or more</p>
| class="confluenceTd" | <p align="center">Unemployed/Part-time</p>
| class="confluenceTd" | <p align="center">Full-time</p>
|-
| class="confluenceTd" | <p align="center">6</p>
| class="confluenceTd" | <p align="center">High school student</p>
| class="confluenceTd" | <p align="center">16 or more</p>
| class="confluenceTd" | <p align="center">Unemployed/Part-time</p>
| class="confluenceTd" | <p align="center">Full-time</p>
|-
| class="confluenceTd" | <p align="center">7</p>
| class="confluenceTd" | <p align="center">Primary school child</p>
| class="confluenceTd" | <p align="center">5-15</p>
| class="confluenceTd" | <p align="center">Unemployed</p>
| class="confluenceTd" | <p align="center">Full-time</p>
|-
| class="confluenceTd" | <p align="center">8</p>
| class="confluenceTd" | <p align="center">Preschool child</p>
| class="confluenceTd" | <p align="center">0-4</p>
| class="confluenceTd" | <p align="center">Unemployed</p>
| class="confluenceTd" | <p align="center">None</p>
|}</div><h4 id="id-3.5SYNTHETICPOPULATION-ControlAttributesandTargetDistributions"><em>Control Attributes and Target Distributions</em></h4><p class="BodyParagraph">There are three major inputs required for population synthesis of which the first step is to identify a set of control attributes and their levels. Next, target distributions of the control attributes and their levels are derived at appropriate geographic units. These target distributions are also known as marginal control totals since they represent the margins of a joint distribution of multiple attributes. Typically, the smallest level of spatial resolution that can be feasibly and reliably used to control attributes is used. If control attribute totals are not accurate at a particular spatial unit, they could be specified at a lower resolution.</p><p class="BodyParagraph">The following considerations are usually important in choosing control variables:</p><ul><li>The number of control variables is important. If there are too few, the synthetic population may not accurately reflect the true population. On the other hand, too many control attributes may cause sample issues. There may not be any sample households with joint attributes of the control variables and this could distort the synthetic population.</li><li>Control attributes may be single or multi-dimensional. Multi-dimensional attributes can be treated as single dimensional attributes with number of categories equal to the product of the numbers of categories in individual attributes. The primary advantage of multi-dimensional attributes is more precise regional control over the correlation between attributes. The disadvantage again is with sparse sample.</li><li>The best choices of variables, will be meaningful attributes that are somewhat “orthogonal” to each other, which means that their variance in the population is largely independent. Conversely, if there are two attributes that are highly correlated, then controlling for both may not achieve much more than controlling for just one.</li><li>Finally, different sets of control attributes may be used for base and forecast years, if limited by forecasting accuracy. This is not necessarily desirable, though. The ability to forecast marginal control totals should be a consideration when specifying control attributes for this base year.</li></ul><p class="BodyParagraph">Target distributions of control variables for the base year could be obtained from a variety of data sources including the following:</p><ul><li>Decennial Census: ~100% sample</li><li>American Community Survey (ACS) summary files: 3% sample, rolling 5-year sample, yields an estimate of ~15% of population</li><li>Bureau of Economic and Business Research (BEBR)</li><li>Census Transportation Planning Products (CTPP)</li><li>Other zonal data developed locally (TAZs)</li></ul><p class="BodyParagraph">For the forecast year, regional socio-economic forecasts or outputs from a land-use model are often used.</p><p class="BodyParagraph">The following table provides the list of control attributes, their geographic and demographic levels along with the relative importance of each control.</p><h4 id="id-3.5SYNTHETICPOPULATION-TABLE3-PopulationSim Controls">TABLE 3-5 PopulationSim Controls</h4><div class="table-wrap">
{| class="wikitable"
|-
! target
! geography
! seed_table
! importance
! control_field
! expression
|-
| num_hh
| MAZ
| households
| 1000000000
| HHS
| (households.WGTP > 0) & (households.WGTP < np.inf)
|-
| hh_size_1
| TAZ
| households
| 5000
| HHSIZE1_S3
| households.NP == 1
|-
| hh_size_2
| TAZ
| households
| 5000
| HHSIZE2_S3
| households.NP == 2
|-
| hh_size_3
| TAZ
| households
| 5000
| HHSIZE3_S3
| households.NP == 3
|-
| hh_size_4
| TAZ
| households
| 5000
| HHSIZE4M_S3
| households.NP >= 4
|-
| hh_age_15_to_44
| TAZ
| households
| 5000
| HHAGE1_S3
| (households.AGEHOH > 14) & (households.AGEHOH <= 44)
|-
| hh_age_45_to_64
| TAZ
| households
| 5000
| HHAGE2_S3
| (households.AGEHOH > 44) & (households.AGEHOH <= 64)
|-
| hh_age_65_abv
| TAZ
| households
| 5000
| HHAGE3_S3
| (households.AGEHOH > 64) & (households.AGEHOH <= np.inf)
|-
| hh_wrks_0
| TAZ
| households
| 5000
| HHWRK1_S3
| households.NWESR == 0
|-
| hh_wrks_1
| TAZ
| households
| 5000
| HHWRK2_S3
| households.NWESR == 1
|-
| hh_wrks_2
| TAZ
| households
| 5000
| HHWRK3_S3
| households.NWESR == 2
|-
| hh_wrks_3m
| TAZ
| households
| 5000
| HHWRK4_S3
| households.NWESR >= 3
|-
| hh_inc_0_25
| TAZ
| households
| 5000
| HHINC1_S3
| (households.HHINCADJ > -999999999) & (households.HHINCADJ <= 24999)
|-
| hh_inc_25_60
| TAZ
| households
| 5000
| HHINC2_S3
| (households.HHINCADJ > 24999) & (households.HHINCADJ <= 59999)
|-
| hh_inc_60_100
| TAZ
| households
| 5000
| HHINC3_S3
| (households.HHINCADJ > 59999) & (households.HHINCADJ <= 99999)
|-
| hh_inc_100_plus
| TAZ
| households
| 5000
| HHINC4_S3
| (households.HHINCADJ > 99999) & (households.HHINCADJ <= 999999999)
|-
| person_male
| SCOUNTY
| persons
| 1000
| MALE_S
| persons.SEX == 1
|-
| person_female
| SCOUNTY
| persons
| 1000
| FEMALE_S
| persons.SEX == 2
|-
| person_age0to4
| SCOUNTY
| persons
| 1000
| AGE0to4_S
| (persons.AGEP > 0) & (persons.AGEP <= 4)
|-
| person_age5to17
| SCOUNTY
| persons
| 1000
| AGE5to17_S
| (persons.AGEP >= 5) & (persons.AGEP <= 17)
|-
| person_age18to24
| SCOUNTY
| persons
| 1000
| AGE18to24_S
| (persons.AGEP >= 18) & (persons.AGEP <= 24)
|-
| person_age25to54
| SCOUNTY
| persons
| 1000
| AGE25to54_S
| (persons.AGEP >= 25) & (persons.AGEP <= 54)
|-
| person_age55m
| SCOUNTY
| persons
| 1000
| AGE55M_S
| persons.AGEP >= 55
|}</div><h4 id="id-3.5SYNTHETICPOPULATION-SampleData"><em>Sample Data</em></h4><p class="BodyParagraph">During population synthesis, individual household and person records are drawn from a disaggregate sample of households to match target distributions of controlled attributes. It may not be possible to control all the desired attributes and so “uncontrolled” attributes are added to the synthetic population from disaggregate sample data. It is essential that the disaggregate sample is representative of the population of the entire region.</p><p class="BodyParagraph">In most cases, the primary source of disaggregate sample data will is Public Use Microdata Sample (PUMS) data, which is now part of the ACS, and follows the same sampling framework, but provides disaggregate records for households and persons across numerous different attributes. PUMS is sampled and grouped according to geographic units, better known as PUMAs. PUMAs cover contiguous areas of roughly 100,000 population, including persons living in group quarters. For example, a metro area of 850,000 might be covered by 8 or more likely 9 PUMAs. In general, ACS-PUMS provides good representative coverage of most regions and is rigorously tested and monitored, so it is was used for creating sample data for this effort.</p><h4 id="id-3.5SYNTHETICPOPULATION-PopulationSim Run"><em>PopulationSim Run</em></h4><p class="BodyParagraph">The control totals and disaggregate sample data are input into a population synthesizer to generate a synthetic population. According to the PopulationSim wiki, 'the objective of a population synthesizer is to generate household weights which satisfies the marginal control distributions. This is achieved by use of a data fitting technique. The most common fitting technique used by various population synthesizers is the Iterative Proportional Fitting (IPF) procedure. Generally, the IPF procedure is used to obtain joint distributions of demographic variables. Then, random sampling from PUMS generates the baseline synthetic population.</p><p class="BodyParagraph">One of the limitations of the simple IPF method is that it does not incorporate both household and person level attributes simulatenously. Some population synthesizers use a heuristic algorithm called the Iterative Proportional Updating Algorithm (IPU) to incorporate both person and household-level variables in the fitting procedure.</p><p class="BodyParagraph">Besides IPF, entropy maximization algorithms have been used as a fitting technique. In most of the entropy based methods, the relative entropy is used as the objective function. The relative entropy based optimization ensures that the least amount of new information is introduced in finding a feasible solution. The base entropy is defined by the initial weights in the seed sample. The weights generated by the entropy maximization algorithm preserves the distribution of initial weights while matching the marginal controls. This is an advantage of the entropy maximization based procedures over the IPF based procedures. PopulationSim uses the entropy maximization based list balancing to match controls specified at various geographic levels.'</p>

3.4 Land Use Data Prep

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[[Category:3.0 Model Design]]

<h2>Network Data Preparation</h2>

This step calculates “nearby” pairs of microzones (MAZ) for shortest distance path calculations. For each MAZ, it selects all MAZs within a buffer and creates pairs of MAZs. These MAZ pairs are input to the next step where DTALite finds the shortest path between them.

'''Tool''': ''Network_DataPrepv2.exe''

'''Directory''': \User.prg\DaySim_Data_Tools

'''Inputs''':

input_node.csv (Node x, y from an all-streets network)

nftpo_MAZs_year.dat (The coordinates of the newly developed microzones)

nftpo_netprep.ctl (Network prep control file)

'''Output''':

input_od_pairs.csv (for input to shortest path update tool)

<h2>Shortest Path Update</h2>

DTALite, a dynamic traffic assignment software, is used to generate node-to-node shortest path distances using the all streets network. The output from this step is used by the DaySim Buffering Tool to prepare the buffered MAZ files.

'''Tool''': ''DTALite.exe''

'''Directory''': \User.prg\DaySim_Data_Tools

'''Inputs''':

input_od_pairs.csv (from the Network Data Preparation tool)

input_node.csv (from all-street network)

input_link_type.csv (from all-street network)

input_link.csv (from all-street network)

DTASettings.ini (settings file)

settings.csv (settings file)

'''Output''':

output_shortest_path.txt (for input to Buffering microzones)

<h2>Buffering & Transit Access Preparation</h2>

In DaySim, it is important to have measures not only of within a particular microzone, but also what lies in the area immediately surrounding each microzone. These measures are created by defining a “buffer” area around each microzone and counting what lies inside the buffer. These variables can then be used in DaySim in a way similar to how zonal land use and density variables are used in TAZ-based models, with the advantage that the buffer is defined in exactly the same way for each microzone. The buffer variables that DaySim uses include:

*The number of households in the buffer;
*Employment (number of jobs) in the buffer in various employment sectors;
*Enrollment in schools in the buffer, segmented by grade schools and colleges;
*The number of spaces and average price of paid off-street parking in the buffer;
*The number of transit stops within the buffer (segmented by sub-mode, if relevant);
*The number of street intersections in the buffer, segmented by 1-node (dead-end or cul-de-sac), 3-node (T-junction), and 4+node intersections; and

In addition, DaySim also uses the distance from the microzone centroid to the nearest transit stop (by transit sub-mode, if relevant), as well as the distance to the nearest open space area while simulating models.

<h3>DaySim Buffering Tool</h3>

A tool to perform the buffer calculations has been developed, and includes a user-friendly GUI. The use of GUI is described in a subsequent chapter of this document. This tool calculates all the buffer and transit access variables that DaySim needs, using the following inputs:

*Base parcel file (obtained from employment and enrollment allocation process)
*Street intersections file
*Transit stops file
*Open spaces/parks file (optional)
*Network nodes file
*Node-to-node shortest path distance file

The input files and their corresponding structures are described in detail in subsequent sections.

Note that it is essential that the buffer measures used in application are consistent with those used for the original model estimation. Thus, when preparing new buffer measures, users should not modify the settings in the buffering tool control file.

<h3>Buffer Calculations</h3>

As mentioned earlier, buffer variables for a parcel are calculated by summing land use variables of all parcels within a certain buffer distance of the particular parcel. In the past, buffer calculations have used a “flat” buffer, using a certain radius, e.g. ¼ mile, and counting everything within that radius and nothing outside the radius. That approach is simple, but has the disadvantages that (a) it weights all opportunities within the buffer the same, whereas in reality the land use that is very close by will tend to have more influence on behavior than the land use at the edge of the buffer, and (b) there can be “cliff effects” if a large development is located near the edge of the buffer. In the latter case, the measures become sensitive to the somewhat arbitrary specification of the buffer size, and to the rules used to deal with parcels that straddle the buffer boundary. This tends to add random “noise” to the buffer measures.

The buffering tool can be set to use flat buffering, or a distance-decay buffer, in which each buffered item is weighted according to the distance from the origin parcel centroid. There are two options provided for the weighting function: a logistic function and a negative exponential function. The logistic form is recommended because its shape is more representative of typical behavioral models that use logistic functions.

The buffering program simultaneously calculates all the buffer variables for two different buffer sizes. The reason for this is that the DaySim choice models use smaller buffers for some variables (e.g. those that represent attractiveness of walk trips), and larger buffers for some other variables (e.g. those that represent attractiveness of bike trips, or more general neighborhood effects).

For distance decay buffering, the user specifies three parameters for each buffer: (1) the distance parameter, (2) the offset parameter, and (3) the slope parameter (the latter two are used only for logistic buffering). The parameters and equation for the logistic curves used for DaySim model estimation and calibration are listed below. It is necessary that these same parameters be used for model application.

Parameter Buffer 1 Buffer 2
Inflection BDIST1 = 660 (ft) BDIST2 = 1320 (ft)
Offset BOFFS1 = 2640 (ft) BOFFS2 = 2640 (ft)
Slope DECAY1 = 0.76 DECAY2 = 0.76
The equation is:
Weight = MIN(1, (1 + Exp(DECAY – 0.5 + BOFFS/5280))
/ (1 + Exp(DECAY * (Distance/BDIST – 0.5 – BOFFS/5280))))

Distance is the distance, in feet, from the origin parcel to any other parcel whose calculation is explained in the next paragraph.

'''FIGURE 3-3 BUFFER1 AND BUFFER2 DISTANCE DECAY WEIGHTS'''

[[File:Figure_3-3.jpg]]

The buffering program also gives the user three options as to how distances are calculated within the buffering program:

#Use crow-fly distance between the XY coordinates
#Use interpolation with a “circuity surface” around each parcel.
#Use shortest path distance between the nearest all street network nodes.

Note that in option 1, because the buffer distance is calculated using XY coordinates from centroid to centroid for parcels, buffering may not be very accurate for parcels that are very large compared to the size of the buffer.

Option 3 provides most accurate distances that take into account obstacles and directness in the street network and is preferable is the required data exists. The following steps are involved in buffering using distance decay weights and all streets network distances:

#The buffering tool first associates each parcel with the nearest network node and creates a parcel -node correspondence.
#Multiple parcels may be associated with a single node and so the base parcel file is reduced to node level by aggregating data from all parcels that are associated with the same node.
#Other items such as open spaces/parks and transit stops are also associated with the closest network nodes.
#At this point, buffering calculations are all done at the node level since node-to-node all street network distance are available. For node pairs that are not within 3 miles of each other, Euclidean distance calculated from XY coordinates is used. Buffer variables for a particular node are calculated by obtaining the weighted sum of land-use variables of all the nodes with the chosen buffer distance. The calculation of distance weights has been described earlier.
#Once the buffer calculations at the node level are complete, the buffer variables are then then transferred to parcels using the parcel -node correspondence created in step 1.

It should also be noted that in case of option 3, during the buffering process, two binary files that have information about node-to-node network shortest path distances are output so the DaySim can use them for simulation of short trips.

The following steps are involved in buffering using distance decay weights and XY/Euclidean distance:

#Calculate distance weights using the logistic decay equation described earlier.
#Calculate buffer variables for each parcel by counting land-use attributes of the surrounding parcels by getting their centroid distances (Euclidean) from that of the parcel under consideration and weighting by the corresponding distance weights.

'''''It must be noted that option 2 – use interpolation with a circuity surface was used when developing the model. However, going forward it is recommended that node-to-node (option 3) method be used for calculating buffer variables.'''''

<h2>Updating Land-use Data</h2>

The user can update the land-use file manually if changes are required in specific zones because of new development, changed land-use etc. The starting point for such changes should be buffering micro-zones step. One of the inputs to the buffering tool is the MAZ file, for example, nftpo_microzones_2015.csv for the 2015 scenario. This file should be manually updated for such changes and saved with the same name. The user should then run the buffer tool and copy the output file (buffered_microzone_2015.dat) to the scenario input directory. The following example describes it step by step.

<h3>Adding 100 Households to an MAZ</h3>

To add 100 additional households to an MAZ, the user needs to run the DaySim data tools, PopulationSim, and the model

#Update the base microzone file (nftpo_microzones_year.csv)
#Run DaySim data tools (DSBuffTool.exe)
#Copy outputs (buffered_maz_year.csv) to the model scenario input directory
#Update PopulationSim controls (update_controls.bat)
#Run PopulationSim
#Convert PopulationSim output to DaySim input (popsim_to_daysim.bat)
#Rename the outputs.
#Copy outputs to the model scenario input directory
#Run the model

The above steps are illustrated in detail in the training slides June2020_Training_NERPM-AB_2.0.pdf here [[Support/Training]]

2.2 Software Requirements

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[[Category:2.0 Hardware & Software Requirements]]

<h1>Operating System</h1>

The model system components are currently configured to run under a variety of Windows versions, including Windows Server 2008 and Windows 7 and more recent updates.

<h2>Cube Voyager</h2>

<h3>Overview</h3>

Cube Voyager is a commercially available software package developed by Citilabs, Inc. for the modeling and analysis of regional passenger transportation systems including roadways, public transit, pedestrians and bicycles. Cube Voyager uses a modular and script-based structure allowing the incorporation of any model methodology ranging from standard four-step models, to discrete choice to activity-based approaches. The software also includes highly flexible network and matrix calculators for the calculation of travel demand and for the detailed comparison of scenarios.

Models built with Cube Voyager can take many forms:

*Four-step models commonly used in urban areas: Cube Voyager includes easy-to-use templates for developing generation, distribution, mode choice and assignment structures
*Activity-based demand: Cube Voyager provides the flexibility to incorporate most of these techniques. It is simple to incorporate specifically designed modules for the analysis and estimation of activity patterns. (eg. the NERPM-AB model system)
*Combined equilibrium models: Cube Voyager provides a complete scripting and equilibrium feedback language allowing models of this form to be implemented.

Past regional travel model systems employed in the region have relied on and made use of the Cube Voyager software. In the new NERPM-AB model many elements of that previous framework are retained since the software and data structures used to represent the supply-side component of the overall model system are unchanged.

Role in the Model System

Cube Voyager serves three (3) important functions in the NERPM-AB model system.

#The software is used to open, view and execute the Cube model catalog file (NERPMAB2.cat) and therefore represents the graphical user interface (GUI) for the entire regional travel model.
#The software includes a built-in Scenario Manager allowing users to modify elements of any desired scenario: defined alternatives (eg. analysis years), applications executed as part of the scenario, the list of data inputs/outputs, and the catalog key parameter values that affect and define different scenarios.
#The software also and most importantly controls the overall model system flow based on the execution of certain model steps in a prescribed sequence using built-in algorithms and user-specified scripts that specify inputs, parameter values and outputs. In addition, the software in the case of NERPM-AB includes the integration between the DaySim activity-based demand model and Cube Voyager supply model where DaySim is called from within the Cube 6 runtime environment.

FDOT has relied on Cube Voyager for previous regional planning models and so these concepts and this regional planning and forecasting software should be familiar to most end-users already practicing in the region.

<h2>DaySim</h2>

<h3>Overview</h3>

The travel demand model used in the Northeast regional planning activity-based model system is coded in a software framework called DaySim. DaySim is one of the two main “families” of activity-based model (AB) systems now being used by MPOs in the United States. DaySim was initially implemented by Mark Bradley and John Bowman in Sacramento, CA, on behalf of the Sacramento Area Council of Governments (SACOG).

DaySim simulates 24-hour itineraries for individuals with spatial resolution as fine as individual parcels and temporal resolution as fine as single minutes, so it can generate outputs at the level of resolution required as input to dynamic traffic simulation. DaySim’s predictions in all dimensions (activity and travel generation, tours and trip-chaining, destinations, modes, and timing) are sensitive to travel times and costs that vary by mode, origin–destination (OD) path, and time of day, so it can, in turn, effectively use as inputs the improved network travel costs and times output from a dynamic traffic simulator. DaySim captures the effects of travel time and cost upon activity and travel choices in a way that is balanced across modes and times of day and consistent with the econometric theory of nested choice models.

The C# (C-sharp) version is used for the NERPM-AB model system, and can be compiled to run in both 32- and 64-bit environments. DaySim can be used in a distributed manner by running separate instances on different processors on different partitions of the study area population, and then merging the results.

<h3>Role in the Model System</h3>

The DaySim activity-based demand model produces six principal outputs: 1) Household file, 2) Household day file, 3) Person file, 4) Person day file, 5) Tour file and 6) Trip file. Taken together, these hierarchical output files are similar to the data files from a traditional household travel diary survey. In this case, however, instead of actual trips from a subsample of the actual population, DaySim produces simulated daily trips for an entire, synthetically generated population of travelers.

<h3>Installation</h3>

No complex installation of the DaySim software is required. DaySim simply resides as a single compiled executable within the NFTPO model directory structure. The compiled executable (DaySim.exe) can be found in the \User.prg\DaySim subdirectory.

<h2>R</h2>

<h3>Overview</h3>

R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical and graphical techniques, and is highly extensible. One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formula where needed.

R is open-source available as Free Software under the terms of the Free Software Foundation's GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes:

*An effective data handling and storage facility,
*A suite of operators for calculations on arrays, in particular matrices,
*A large, coherent, integrated collection of intermediate tools for data analysis,
*Graphical facilities for data analysis and display either on-screen or on hardcopy, and
*A well-developed, simple and effective programming language that includes conditionals, loops, user-defined recursive functions and input and output facilities.

More information on R can be found at: http://www.r-project.org/.

<h3>Role in the Model System</h3>

R is not actively used in this model. R is supplied with the model to generate DaySim summaries for calibration and validation.

Once DaySim is run and day patterns of all the persons in the model system have been simulated, R-scripts are also used to prepare summaries of various sub-models outputs such as auto ownership, tour/trip modes and times, etc. Summary tables are written out to Excel spreadsheets that are subsequently used calibration and validation of the AB demand model.

<h3>Installation</h3>

No complex installation of the R software is required. R simply resides as a single compiled executable within the NFTPO model directory structure. The R application can be found in the \R-3.4.4 subdirectory.

<h2>PopulationSim</h2>

<h3>Overview</h3>

PopulationSim is an open platform for population synthesis and survey weighting. It is a Python-based tool for population synthesis which includes an easy-to-use interface. More information on PopulationSim can be found at the ActivitySim website: https://activitysim.github.io/populationsim/index.html

<h3>Role in the Model System</h3>

Activity Based Models (ABMs) operate in a micro-simulation framework, wherein the travel choices of person and household decision-making agents are predicted by applying Monte Carlo methods to behavioral models. This requires a data set of households and persons representing the entire population in the modeling region. Population synthesis refers to the process used to create this data.

<h3>Installation</h3>

PopulationSim required 64-bit Anaconda (Python 2). No complex installation of the Anaconda software is required. Anaconda simply resides in \User.prg\Population_Synthesis\Anaconda2 within the NFTPO model directory structure. Anaconda is an open-source distribution of Python which bundles the most useful python libraries together for making it easy for the users.

The developers of PopulationSim have created a detailed stand-alone wiki which provides clear instructions, steps and visual cues to walk a new user through the installation or update of both the supplementary software and PopulationSim. The wiki also describes how to use the software, how to validate model results and also describes how PopulationSim works in detail.

3.7 Configuration

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Bstabler:

[[Category:3.0 Model Design]]

The configuration file is the main control file for DaySim. The configuration file informs DaySim about inputs/outputs and various model settings. These settings include input/output file names, types and locations, sample rates, DaySim pathbuilding weights, and also allow users to specify which DaySim model components should be executed. A detailed description of the setting used in the NERPM-AB model is provided in section 4.3.

<h2>Seed Shadow Price</h2>

Shadow prices are constants that are added to individual microzones during the simulation of work and school location choice models. This is done to result in a good match between the following:

*Work locations of workers and employment
*School locations of students and enrollment

In DaySim, the long-term models (work and school location choice) are run ten times while updating shadow prices for all microzones during every iteration to create a seed shadow price file to be used as input to the full model run. The intent is to make sure that there is a good match between the number of job available in a microzones and the number of workers choosing the microzones as their usual work location. The same applies for students and school locations.

When the user updates the employment and/or school enrollment data, the Shadow Price step should be rerun.

Support/Training

2020-08-28T17:44:41Z

Bstabler:

For support, please first check "[[Frequently Asked Questions]]."

*[[Media:June2020_Training_NERPM-AB_2.0.pdf|NERPMAB2_Update.pdf]]
*[https://vimeo.com/452635204 NERPMAB2 Webinar Video Recording]

*[[Media:NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf|NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf]]
*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording]

*[[Media:NERPMAB1v2_Training_June_2016.pdf|NERPMABv2 Training Slides]]
*[https://youtu.be/IApPyy-Zi_s NERPMAB1v2 Webinar, June 15, 2016]
*[[Media:NERPMAB1v2 Webinar Excercise Revised (2).pdf|NERPMABv2 Exercise]]

If you need additional information, please contact [mailto:mlocklear@northfloridatpo.com?cc=Stephen.Lawe@rsginc.com;Ameera.Sayeed@dot.state.fl.us;Hadi.Sadrsadat@rsginc.com Milton Locklear]

Thank you!

Support/Training

2020-08-28T17:43:55Z

Bstabler:

For support, please first check "[[Frequently Asked Questions]]."

*[[Media:June2020_Training_NERPM-AB_2.0.pdf|NERPMAB2_Update.pdf]]
*[https://vimeo.com/452635204 NERPMAB2 Webinar Video Recording]

*[[Media:NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf|NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf]]
*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording]

*[[Media:NERPMAB1v2_Training_June_2016.pdf|NERPMABv2 Training Slides]]
*[https://youtu.be/IApPyy-Zi_s NERPMAB1v2 Webinar, June 15, 2016]
*[[Media:NERPMAB1v2 Webinar Excercise Revised (2).pdf|NERPMABv2 Exercise]]

*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording]
*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording]

If you need additional information, please contact [mailto:mlocklear@northfloridatpo.com?cc=Stephen.Lawe@rsginc.com;Ameera.Sayeed@dot.state.fl.us;Hadi.Sadrsadat@rsginc.com Milton Locklear]

Thank you!

Support/Training

2020-08-28T17:42:08Z

Bstabler:

For support, please first check "[[Frequently Asked Questions]]."

*[[Media:June2020_Training_NERPM-AB_2.0.pdf|June2020_Training_NERPM-AB_2.0.pdf]]
*[https://vimeo.com/452635204 NERPMAB2 Webinar Video Recording]

*[[Media:NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf|NERPMAB1v3_Update_Overview_Amended_9_4_2017.pdf]]
*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording]

*[[Media:NERPMAB1v2_Training_June_2016.pdf|NERPMABv2 Training Slides]]
*[https://youtu.be/IApPyy-Zi_s NERPMAB1v2 Webinar, June 15, 2016]
*[[Media:NERPMAB1v2 Webinar Excercise Revised (2).pdf|NERPMABv2 Exercise]]

*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording]
*[https://vimeo.com/202441046 NERPMAB1v3 Webinar Video Recording]

If you need additional information, please contact [mailto:mlocklear@northfloridatpo.com?cc=Stephen.Lawe@rsginc.com;Ameera.Sayeed@dot.state.fl.us;Hadi.Sadrsadat@rsginc.com Milton Locklear]

Thank you!

7.3 Version NERPMAB2

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7.3 Version NERPMAB2

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