Performance Evaluation of Station-Based Autonomous On-Demand Car-Sharing Systems

Abstract

Autonomous Mobility-on-Demand (AMoD) systems hold potential promise for addressing urban mobility challenges. Their key principle is to utilize fleets of shared self-driving vehicles to respond to customer demand on flexible routes in real-time. This research investigates station-based AMoD car-sharing systems and uses scenario analyses to identify plausible future paths for their deployment. A traffic simulation model which implements real-time rebalancing of idle vehicles is developed to evaluate their performance under uncertain travel demands. Unlike other literature which assumed homogeneous demand and resulted in low increases in vehicle kilometers travelled (VKT), this study relied on realistic heterogeneous demand and showed a significant increase in VKT. A case study for Melbourne demonstrated the impacts and showed that while AMoD can meet the demand for travel using only 16% of the current vehicle fleet, they would produce 77% increase in VKT. This would significantly increase congestion in any real-world scenario and goes against the hype of AMoD being the answer to congestion problems.