Fleet Re-Balancing with In-Route Charging for Multi-Class Autonomous Electric MoD Systems

Autonomous electric mobility on demand (AEMoD) services are anticipated to be the future of private transportation, serving tens-of-thousands of requests per minute in large cities. To cope with this massive demand, a decentralized (i.e., zone-based) and multi-class management framework of AEMoD fleets was recently introduced. Yet, the inter-zone management of such approach has not been investigated. This paper thus fills this gap by studying the fleet re-balancing problem, with possible in-route charging, in decentralized multiclass AEMoD systems. A queuing model for multi-class re-balancing and possible in-route charging is developed on top of the system's decentralized fleet management. The stability conditions of this model are first derived, then the optimal inter-zone multi-class re-balancing and in-route charging decisions are derived so as to minimize the maximum response time in each deficient zone. Closed-form solutions are derived using Lagrangian analysis and simulations in a realistic setting in the city of Seattle are employed to illustrate the merits of our proposed re-balancing scheme as opposed to different baseline rebalancing approaches.