Modeling and Optimizing Shared Autonomous Vehicle Fleets for Long-Distance Intercity Travel

Abstract

This study examines the optimal fleet sizing of shared autonomous vehicles (SAVs) considering long-distance intercity travel. A network of cities is modeled, with each city characterized by its population, attractiveness, and location. In each city, a fleet covering 10% of the population serves daily intra-city needs. However, intercity movements, reaching up to 20% of the population during peak periods, create additional demand, requiring a reevaluation of fleet size for both local and intercity needs. The methodology uses daily simulations over 30 days, with statistical distributions modeling departures and returns, and dynamic equations adjusting fleet size. Results show that interconnecting more cities increases fleet requirements to 13% of the population nationwide. Extending the network to include additional destinations of similar size and attractiveness stabilizes fleet needs at around 12%, highlighting the importance of large-scale integration.