Improved Travel Demand Modeling with Synthetic Populations

Kaidi Wang, Wenwen Zhang, Henning Mortveit, Samarth Swarup

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We compare synthetic population-based travel demand modeling with the state of the art travel demand models used by metropolitan planning offices in the United States. Our comparison of the models for three US cities shows that synthetic population-based models match the state of the art models closely for the temporal trip distributions and the spatial distribution of destinations. The advantages of the synthetic population-based method are that it provides greater spatial resolution, can be generalized to any region, and can be used for studying correlations with demographics and activity types, which are useful for modeling the effects of policy changes.

Original languageEnglish (US)
Title of host publicationMulti-Agent-Based Simulation XXI - 21st International Workshop, MABS 2020, Revised Selected Papers
EditorsSamarth Swarup, Bastin Tony Savarimuthu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages94-105
Number of pages12
ISBN (Print)9783030668877
DOIs
StatePublished - 2021
Externally publishedYes
Event20th International Workshop on Multi-Agent-Based Simulation, MABS 2020 - Auckland, New Zealand
Duration: May 10 2020May 10 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12316 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Workshop on Multi-Agent-Based Simulation, MABS 2020
Country/TerritoryNew Zealand
CityAuckland
Period5/10/205/10/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • Synthetic population
  • Transportation
  • Travel demand

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