The Travel and Environmental Implications of Shared Autonomous Vehicles, Using Agent-Based Model Scenarios

Car sharing programs like Car2Go and ZipCar have quickly expanded, with the number of United States (US) users doubling every one to two years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous vehicles will address many current car-sharing barriers, including users’ travel to access available vehicles. This work describes the design of an agent-based model for Shared Autonomous Vehicle (SAV) operations, the results of many case study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use settings. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips. Next, the model is run over one hundred days with driverless vehicles ferrying travelers from one destination to the next. During each 5-minute interval, some unused SAVs relocate to shorten wait times for next-period travelers. Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.