This paper presents a two-layered rebalancing model tailored for Shared Automated Electric Vehicle (SAEV) systems. The first layer aims to narrow the gap between supply and demand in different zones, while the second layer focuses on minimizing rebalancing costs. The model offers seamless integration with various dispatching models. We evaluate the model's efficacy through agent-based simulations using Yellow Taxi data from Midtown Manhattan. Results show that implementing this model can reduce customers' average waiting time by approximately 10% compared to scenarios without rebalancing. Moreover, our study reveals that higher accuracy in demand prediction models leads to improved rebalancing performance.
A Two-Layered Rebalancing Model for Shared Automated Electric Vehicles
24.09.2024
625363 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch