Integrated Model of Travel Demand and Network Simulation

The authors' paper describes a new approach to integrate an Activity‐Based travel demand Model (ABM) and Dynamic Traffic Assignment (DTA) model, taking maximum advantage of the disaggregate nature of both models.  This approach is referred to as “deep integration”.  With this approach, all interaction between the ABM and DTA is implemented at the individual level without an aggregation bias.  Vehicle trips are generated by ABM for DTA and Level‐of‐Service (LOS) variables are determined by DTA for the ABM.  The paper suggests solutions for several long‐standing issues in ABM‐DTA integration such as achieving logical consistency between activity durations and travel times at the individual level and using individual trajectories generated by DTA as a source of LOS for ABM.  The developed ABM‐DTA integration system includes two levels of equilibration: 1) external loop that includes a generation of a complete daily activity pattern, and 2) internal loop that includes equilibration of individual daily schedules, trip departure times, and route choices. The paper is based on project research for the Atlanta Regional Commission (ARC) and Ohio State DOT (ODOT) sponsored by the FHWA C10 grants.  It describes the results of application of the developed integrated ABM‐DTA system for real‐size regional networks of Atlanta, GA and Columbus, OH.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.
  • Authors:
    • Vovsha, Peter
    • Hicks, James E
    • Stratton, Matt
    • Tung, Robert
    • Anderson, Rebekah
    • Giaimo, Gregory
    • Rousseau, Guy
  • Conference:
  • Date: 2018

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p

Subject/Index Terms

Filing Info

  • Accession Number: 01661391
  • Record Type: Publication
  • Report/Paper Numbers: 18-05502
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 27 2018 9:45AM