Travel demand generation using Bayesian Networks: an application to Switzerland

Thanks to their ability to simulate the travel behavior at the individual scale, agent-based models have gained popularity over the last years. These models are data-intensive, with regards to transport supply and demand. In particular, a detailed description of the population and its travel behavior is required. Bayesian Networks (BNs) are directed acyclic graphs representing joint probability distributions. They have recently been employed for population synthesis and daily activity patterns generation in studies showing that BNs effectively capture the causality links existing between variables and are easily interpretable. Moreover, given their flexible structure, BNs can be adapted for situations in which data from various sources are combined. In this paper, the authors' goal is to estimate a BN for both population and activity pattern synthesis in Switzerland. The authors evaluate the performance of this approach compared to the statistical matching algorithm using aggregated and disaggregated metrics. In particular, the authors show that understanding the dependency structure linking the population characteristics and its mobility behavior is key to generate representative synthetic agents and daily activity patterns. This study is a contribution towards the development of interpretable, flexible and behaviorally rich travel demand generation models.

• Record URL:
  • https://doi.org/10.1016/j.procs.2023.03.035
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1877050923005707
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier. The 14th International Conference on Ambient Systems, Networks and Technologies (ANT) March 15-17, 2023, Leuven, Belgium.
• Authors:
  • Sallard, Aurore
  • Balać, Miloš
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 267-274
• Serial:
  • Procedia Computer Science
  • Volume: 220
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Activity choices; Bayes' theorem; Population; Travel demand; Travel patterns
• Geographic Terms: Switzerland
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01890394
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 22 2023 5:07PM