Benefits and Costs of Ride-Sharing in Shared Automated Vehicles across Austin, Texas: Opportunities for Congestion Pricing

Abstract

A self-driving, fully automated, or “autonomous” vehicle (AV) revolution is imminent, with the potential to eliminate driver costs and driver error, while ushering in an era of shared mobility. Dynamic ride-sharing (DRS), which refers to sharing rides with strangers en route, is growing, with top transportation network companies providing such services. This work uses an agent-based simulation tool called MATSim to simulate travel patterns in Austin, Texas in the presence of personal AVs, and shared AVs (SAVs), with DRS and advanced road-pricing policies in place. Fleet size, pricing, and fare level impacts are analyzed in depth to provide insight into how SAVs may best be introduced to a city or region. Results indicate that the cost-effectiveness of traveling with strangers overcomes inconvenience and privacy issues at moderate-to-low fare levels, with high fares being more detrimental than the base case. A moderately sized Austin, Texas fleet (one SAV for every 25 people) serves nearly 30% of all trips made during the day. The average vehicle occupancy of this fleet was around 1.48 [after including the 12.7% of SAV vehicle-miles traveled (VMT) empty/without passengers], with a 4.5% increase in VMT. This same fleet performs better when road-pricing is enforced in the peak periods (4 h a day), moderating VMT by 2%, increasing SAV demand and in turn fleet-manager revenue. SAVs are able to earn around $100 per SAV per day even after paying tolls, but only at low-fare levels.