This study set out to model and simulate the travel and revenue impacts of a fleet of automated vehicles (AVs) as a potential solution for the first-mile problem of transit access to help lower barriers to potential riders in the San Francisco Bay Area.
The simulation was studied over three different scenarios, each offering a range of fares:
- Pick Up and Drop Off Points (PUDO), which involves eight-seat automated vehicles providing a ridesharing service that picks up riders who walk from the transit drop off point to their home.
- Door-to-door, single-passenger ride hailing service (D2D Single).
- Door-to-door ridesharing (D2D Ridesharing), where eight-seat automated vehicles provide a home-based pickup ridesharing service.
A multi-agent-based simulation model was used and the effects of AVs on vehicle miles traveled (VMT), travel time, operation cost, and revenue were estimated.
The study used BA-MATSim, a Bay Area simulation model with a module that simulates the effects of automated taxi fleets. The algorithm dispatches a fleet of empty, driverless vehicles to fulfill pickup requests, with vehicles selected based on proximity. The research team created a fleet size of 5,000 vehicles that started their day randomly throughout the model area. Pickup and drop-off locations were determined by an optimization framework in which walking and wait times are minimized.
In combination with AV fleet dispatch optimization, different pricing was applied to the automated taxis to determine the impact of fare differences on ridership and revenue (pricing ranged from $0 to $10 per ride based on current prices for microtransit services). Revenue derived from the automated taxis were compared to the costs of operating the automated taxis per mile, using an estimated range of costs from $0.30 to $0.50 per mile.