Maximum Throughput Dispatch for Shared Autonomous Vehicles Including Vehicle Rebalancing

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 9; pp. 1 - 15
• Main Authors: Robbennolt, Jake, Levin, Michael W.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous vehicles; Heuristic algorithms; Mathematical models; Maximum throughout; Passengers; Public transportation; Queuing theory; rebalancing; Reinforcement learning; shared autonomous vehicles; Simulation; Stability analysis; Throughput; Urban areas; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3268158

Shared autonomous vehicles (SAVs) provide on demand point-to-point transportation for passengers. This service has been extensively studied using dispatch heuristics and agent based simulations of large urban areas. However, these approaches make no mathematical guarantees of the passenger throughput for the SAV network. This study builds on the dynamic queuing model design of Kang and Levin which provides a maximum stability dispatch policy for SAVs. This model is extended to include rebalancing of empty vehicles to regions of high demand. The modified dispatch policy is proven to maximize throughput. Simulation results show that this dispatch policy reduces waiting times (between vehicle dispatch and passenger pickup) compared to the original formulation. However, vehicle time traveling empty increases in some scenarios. Simulation results also show that rebalancing often reduces passenger waiting times, but not when too many vehicles rebalance at once and are not available for dispatch.