Enhancing urban traffic management through shared autonomous electric vehicles and dynamic simulation.

Abstract

In the face of rapid urbanization and the increasing number of vehicles, urban centers are struggling with traffic congestion. This study presents a dynamic travel strategy using the MATSim platform to schedule urban travel, incorporating a model for shared autonomous electric vehicles. The model is evaluated using a baseline scenario for Shanghai, exploring the effects of vehicle range, charging capabilities, and power supply strategies on the uptake of shared autonomous electric vehicles. Results indicate that enhancements in vehicle range and charging efficiency slightly decrease the use of autonomous vehicles by 2.5%, as the existing vehicle specifications already meet daily travel needs in Shanghai. Additionally, the transition from traditional charging stations to a battery-swapping system does not significantly alter overall travel behavior of shared autonomous electric vehicles. These findings provide insights into the deployment of intelligent traffic systems to alleviate urban traffic congestion.