Experts predict that vehicles could be fully automated by as early as 2025 or as late as 2035. Methods are needed to help the public and private sector understand automated vehicle technologies and their system-level effects. In this project, the researchers explore the effects of automated vehicles using the San Francisco Bay Area Metropolitan Transportation Commission’s activity-based travel demand model (MTC-ABM). The simulation is unique in that it articulates the size and direction of change on travel for a wide range of automated vehicles scenarios. Second, they simulate the effects of the introduction of an automated taxi service on conventional personal vehicle and transit travel in the San Francisco Bay Area region and use new research on the costs of automated vehicles to represent plausible per mile automated taxi fares. The team use an integrated model for the San Francisco Bay Area that includes the MTC-ABM combined with the agent-based MATSim model customized for the region. This set of models uses baseline travel demand data from the region’s official activity-based travel model and dynamically assigns vehicles on road and transit networks by the time of day. Finally, the researchers use the MTC-ABM and the MATSim dynamic assignment model to simulate different 7 “first” mile transit access services, including ride-hailing (Uber and Lyft) and ridesharing (Uber Pool/Lyft Line and Via) with and without automated vehicles. The results provide insight into the relative benefits of each service and automated vehicle technology and the potential market for these services.
Automated Vehicle Scenarios: Simulation of System-Level Travel Effect in San Francisco Bay Area by Caroline Rodier, Miguel Jaller, Elham Pourrahmani, Joschka Bischoff, Joel Freedman, and Anmol Pahwa (REPORT)