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.
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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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