Congestion-Aware Rideshare Dispatch for Shared Autonomous Electric Vehicle Fleets

Abstract

The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively.

A survey on emergent trends in the optimization of car‐sharing systems

“…Huang et al. (2022) propose an optimization framework for dealing with the EAV routing and charging decisions. The activities of both relocating and charging EAVs are considered in the integrated system, Safari, proposed by Wang et al.…”

“…The proposed reliability-based design optimization approach aims at minimizing the total system cost due to the fleet, electricity, and charging station operations. Huang et al (2022) propose an optimization framework for dealing with the EAV routing and charging decisions. The activities of both relocating and charging EAVs are considered in the integrated system, Safari, proposed by Wang et al (2022a) in which a dynamic deadlinebased deep reinforcement learning algorithm is developed.…”