Automated Vehicle Scenarios: Simulation of System-Level Travel Effects Using Agent-Based Demand and Supply Models in the San Francisco Bay Area

Experts predict that vehicles could be fully automated by as early as 2025 or as late as 2035. Understanding the potential impacts of automated vehicle (AV) technologies and services is critical to maximizing multi-modal accessibility and minimizing negative environmental effects. The project will apply two integrated disaggregate agent-based travel models (San Francisco Bay Area Activity Base Travel and dynamic assignment [MATsim]) to simulate three AV scenarios: (1) personal AVs, (2) an automated taxi service, and (3) an automated shared-taxi service. The project will evaluate the results to explore the significance and relative magnitude of (1) induced travel effects due to increased access to vehicles and reduced auto travel time, (2) empty vehicle relocation travel (e.g., to serve other family members or customers), and (3) queuing and congestion effects of drop off and pick up travel, instead of parking during peak travel periods (e.g., 8 am arrival times in central business districts). The proposed study represents a significant advancement over the previous limited research by improving the realism of the spatial and temporal dimensions of supply and demand. The study will provide early insights into sustainable AV systems.


  • English


  • Status: Active
  • Funding: $91641
  • Sponsor Organizations:

    California Department of Transportation

    1227 O Street
    Sacramento, CA  United States  95843
  • Performing Organizations:

    National Center for Sustainable Transportation

    University of California, Davis
    Davis, CA  United States 
  • Principal Investigators:

    Rodier, Caroline

  • Start Date: 20160901
  • Expected Completion Date: 20180630
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01610329
  • Record Type: Research project
  • Source Agency: National Center for Sustainable Transportation
  • Files: UTC, RiP
  • Created Date: Sep 6 2016 5:26PM