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International Workshop on Multi-Agent Systems and Agent-Based Simulation

MABS 2020: Multi-Agent-Based Simulation XXI pp 94–105Cite as

Improved Travel Demand Modeling with Synthetic Populations

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12316))

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.

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Acknowledgments

We thank our external collaborators and members of the Network Systems Science and Advanced Computing (NSSAC) division for their suggestions and comments. This work was partially supported by DTRA Grant HDTRA1-17-F-0118 and NASA grant 80NSSC18K1594. We also thank Atlanta Regional Commission (ARC), Puget Sound Regional Council (PSRC) and Richmond Regional Planning District Commission for providing us with travel demand model outputs.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views, official policies or endorsements, either expressed or implied, of NASA, DTRA, or the U.S. Government.

Author information

Authors and Affiliations

  1. Urban Affairs and Planning, Virginia Tech, Blacksburg, VA, 24061, USA

    Kaidi Wang & Wenwen Zhang

  2. Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, 22911, USA

    Henning Mortveit

  3. Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, 22911, USA

    Henning Mortveit & Samarth Swarup

Authors
  1. Kaidi Wang
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  2. Wenwen Zhang
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  3. Henning Mortveit
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  4. Samarth Swarup
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Corresponding author

Correspondence to Kaidi Wang .

Editor information

Editors and Affiliations

  1. University of Virginia, Charlottesville, VA, USA

    Samarth Swarup

  2. Department of Information Science, University of Otago, Dunedin, New Zealand

    Bastin Tony Roy Savarimuthu

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Wang, K., Zhang, W., Mortveit, H., Swarup, S. (2021). Improved Travel Demand Modeling with Synthetic Populations. In: Swarup, S., Savarimuthu, B.T.R. (eds) Multi-Agent-Based Simulation XXI. MABS 2020. Lecture Notes in Computer Science(), vol 12316. Springer, Cham. https://doi.org/10.1007/978-3-030-66888-4_8

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  • DOI: https://doi.org/10.1007/978-3-030-66888-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66887-7

  • Online ISBN: 978-3-030-66888-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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