1.2 Model Sensitivities - Revision history

Bpaul at 19:34, 9 March 2023

2023-03-09T19:34:14Z

Bstabler at 18:58, 25 August 2020

2020-08-25T18:58:12Z

Mlocklear at 16:31, 31 January 2017

2017-01-31T16:31:39Z

Gwineman: Created page with "Category:1.0 Overview '''Transportation Investments & Policies ''' NERPM-AB system is designed to be sensitive to a wide variety of transportation investments and policie..."

2016-10-17T19:19:00Z

Created page with "Category:1.0 Overview '''Transportation Investments & Policies ''' NERPM-AB system is designed to be sensitive to a wide variety of transportation investments and policie..."

New page

[[Category:1.0 Overview]]

'''Transportation Investments & Policies
'''
NERPM-AB system is designed to be sensitive to a wide variety of transportation investments and policies. The model system incorporates representations of roadway and transit networks that can be easily configured to represent changes in network capacity or service provision. Detailed pathtype alternatives such as roadway toll and no-toll paths and transit submodes are explicitly represented in the model system. In addition, the model incorporates distributed values-of-time across the population, dependent of travel purpose, income and other factors, providing a more realistic representation of travelers’ willingness-to-pay. The model system’s explicit representation of each individual’s entire daily activity pattern also provides the opportunity to better represent the impacts of travel demand management scenarios.

'''Land Use'''

One of the distinguishing features of the NERPM-AB is the use of parcels as the basic spatial unit for generating travel demand. Use of this fine-grained geography, in conjunction with “all streets” based network impedances, provides the model with an enhanced ability to represent detailed urban form and land use characteristics that are particularly important when considering non-motorized travel such as walking and bicycling. In addition, the model predicts demand for seven different activity types using detailed land use information such employment by nine industrial sectors and enrollment by school type.

'''Socioeconomics & Demographics'''

NERPM-AB uses a “synthetic population” to predict individual and household level travel choices. The synthetic population is comprised of lists of households and persons that are based on observed or forecasted distributions of socioeconomic attributes and are typically created by sampling detailed Census microdata. These lists function as the basis for all subsequent choice making simulated in the activity-based model. Use of a disaggregate representation of the population reduces aggregation bias in the model system, and allows the model to more realistically incorporate the influence of socioeconomic and demographic information on individual and household travel choices.

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 '''Transportation Investments & Policies
 '''
-NERPM-AB system is designed to be sensitive to a wide variety of transportation investments and policies.  The model system incorporates representations of roadway and transit networks that can be easily configured to represent changes in network capacity or service provision.  Detailed pathtype alternatives such as roadway toll and no-toll paths and transit submodes are explicitly represented in the model system.  In addition, the model incorporates distributed values-of-time across the population, dependent of travel purpose, income and other factors, providing a more realistic representation of travelers’ willingness-to-pay.  The model system’s explicit representation of each individual’s entire daily activity pattern also provides the opportunity to better represent the impacts of travel demand management scenarios.
+NERPM-AB system is designed to be sensitive to a wide variety of transportation investments and policies.  The model system incorporates representations of roadway and transit networks that can be easily configured to represent changes in network capacity or service provision.  Detailed pathtype alternatives such as roadway toll and no-toll paths and transit submodes are explicitly represented in the model system.  In addition, the model incorporates distributed values-of-time across the population, dependent on travel purpose, income, and other factors, providing a more realistic representation of travelers’ willingness-to-pay.  The model system’s explicit representation of each individual’s entire daily activity pattern also provides the opportunity to better represent the impacts of travel demand management scenarios.
 '''Land Use'''

@@ Line 9: / Line 9: @@
 '''Land Use'''
-One of the distinguishing features of the NERPM-AB is the use of parcels as the basic spatial unit for generating travel demand.  Use of this fine-grained geography, in conjunction with “all streets” based network impedances, provides the model with an enhanced ability to represent detailed urban form and land use characteristics that are particularly important when considering non-motorized travel such as walking and bicycling.  In addition, the model predicts demand for seven different activity types using detailed land use information such employment by nine industrial sectors and enrollment by school type.
+One of the distinguishing features of the NERPM-AB is the use of microzones as the basic spatial unit for generating travel demand.  Use of this fine-grained geography, in conjunction with “all streets” based network impedances, provides the model with an enhanced ability to represent detailed urban form and land use characteristics that are particularly important when considering non-motorized travel such as walking and bicycling.  In addition, the model predicts demand for seven different activity types using detailed land use information such employment by nine industrial sectors and enrollment by school type.
 '''Socioeconomics & Demographics'''
 NERPM-AB uses a “synthetic population” to predict individual and household level travel choices.  The synthetic population is comprised of lists of households and persons that are based on observed or forecasted distributions of socioeconomic attributes and are typically created by sampling detailed Census microdata.  These lists function as the basis for all subsequent choice making simulated in the activity-based model.  Use of a disaggregate representation of the population reduces aggregation bias in the model system, and allows the model to more realistically incorporate the influence of socioeconomic and demographic information on individual and household travel choices.