Citation

Afandizadeh S, Mosavi M, Mirzahossein H. J. Transp. Res. (Tehran) 2023; 20(2): 1-16.

Copyright

(Copyright © 2023, Iran University of Science and technology, Transportation Research Institute)

DOI

10.22034/tri.2022.325658.3006

PMID

unavailable

Abstract

To make informed decisions about transportation infrastructure planning, planners and engineers must be able to anticipate the response to transportation demand by changing the characteristics of the transportation system and the characteristics of the people using the transportation system. For this purpose, travel demand models are used. The first generation of demand models are travel based models. Activity based models were developed due to insufficient accuracy of four-step models predicting. . In this study, in order to predict activity based travel demand, some software models were modified by models made in previous studies. According to the study area. in the next step, data on synthetic population, individuals, households, traffic zones, land use information, and impedance matrices between Washington, DC, areas were entered into the Activitysim software. in the next step, the travel information obtained from the Software was compared with the actual travel data in the study area and also with the number of trips of the first version of Actgen software. Comparisons of travel matrices indicate that the focus of travel according to the output of Activitysim software models is higher than the actual travel data, and the travel matrices of Actgen software provides more dispersion. also, in the number of trips based on travel mode and different trip destination, we see the proximity of the Activitysim models outputs and actual data. actual basic information about the distribution of travel by different age groups, income groups, household size, number of cars, gender distribution of the population and the approximate proximity of these data to the number of travel outputs of implemented software models show the accuracy of input information and has high analytical power of software models as well as modified models based on the study area.

Language: en