Li, Shuichao (Fuzhou University), Li, Li (Fuzhou University), Jiang, Xiquan (Fuzhou University), Lin, Dianchao (Fuzhou University), Hu, Xiaoxi (Beijing Jiaotong University)

A Two-Layered Rebalancing Model for Shared Automated Electric Vehicles

Scheduled for presentation during the Regular Session "Transport planning" (FrAT9), Friday, September 27, 2024, 10:30−10:50, Salon 17

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

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.