Real-Time Operations of Autonomous Mobility- on-Demand Services With Inter-and Intra-Zonal Relocation

In the context of shared connected autonomous vehicles (SCAVs), the relocation of idle vehicles is a crucial issue for the operation of autonomous mobility-on-demand (AMoD) services. Unlike traditional human-chauffeured taxis, AMoD operations are fully controllable by central systems and not affected by unpredictable human driver behavior. To address the spatial-temporal imbalance between supply and demand and optimize the level of service while minimizing agency costs, we propose a real-time AMoD relocation model. However, vehicle-specific control every second for large fleet sizes may cause computational burdens for the control center. To overcome this, we present a bi-level framework that decomposes the original system-level problem into an inter-zonal relocation problem for the entire service area and an intra-zonal relocation problem for each zone. This reduces the decision space to periodic inter- and intra-zonal relocation of idle vehicles. Using real-world taxi operation data from Daejeon City, Korea, we demonstrate the proposed method via agent-based simulations, assuming that SCAVs replace existing taxis. The results show that the method can significantly reduce the total generalized cost for both users and the agency. Through a sensitivity analysis, we investigate how the performance varies depending on the zone size, inter- and intra-zonal relocation interval, and demand uncertainty and discuss the observed tradeoff. The intended contribution is twofold: first, we propose a novel computationally feasible method that can efficiently operate AMoD systems in real time; second, we provide a closed-form analytical formulation that can help decision-makers explicitly understand the relationship between the cost components and the decision factors.