Congestion-Aware Routing, Rebalancing, and Charging Scheduling for Electric Autonomous Mobility-on-Demand System

TL;DR

This paper proposes a congestion-aware optimization framework for routing, rebalancing, and charging in electric autonomous mobility-on-demand systems, aiming to improve traffic flow and energy management.

Contribution

It introduces a novel optimization model with heuristic algorithms that incorporate traffic congestion and energy constraints for electric autonomous vehicles.

Findings

Effective routing and rebalancing strategies under congestion

Energy constraints satisfied in re-routing solutions

Simulation results demonstrate improved traffic flow and energy efficiency

Abstract

In this paper, we investigate the problem of routing, rebalancing, and charging for electric autonomous mobility-on-demand systems concerning traffic congestion. We analyze the problem at the macroscopical level and use a volume-delay function to capture traffic congestion. To address this problem, we first formulate an optimization problem for routing and rebalancing. Then, we present heuristic algorithms to find the loop of the traffic flow and examine the energy constraints within the resulting loop. We impose charging constraints on the re-routing problem so that the new solution satisfies the energy constraint. Finally, we verify the effectiveness of our method through simulation.