A Demand Prediction-Driven Algorithm for Dynamic Shared Autonomous Vehicle Relocation: Integrating Deep Learning and System Optimization

Hui-Yong Zhang, Kun Zhao, Wei-Xin Yu, Meng Zeng, Si-Qi Wang, Fang Zong

This paper develops a dynamic repositioning mechanism for shared autonomous vehicles (SAVs) driven by travel demand. A prediction model for SAV travel demand is constructed by the proposed GRU-FC network. On this basis, an integer programming model for empty-vehicle dispatching which aims to maximize the SAV revenue while minimizing the costs of vehicle relocation and operation is formulated. The results indicate that, relative to relying solely on natural vehicle dispatching, the proposed dispatching scheme reduces empty vehicle dispatches by 21.00% and increases total system profit by 38.89%. The findings theoretically improve the dynamic optimization theory of SAV dispatching and provide theoretical support for algorithm design based on the “demand-pull” principle. The method proposed in this paper is beneficial to optimizing the dynamic vehicle dispatching theory of SAVs. It helps to boost system revenue, reduce empty driving costs, alleviate traffic pressure, and lower energy consumption and environmental pollution, thereby fostering sustainable urban mobility and supporting the Sustainable Development Goals of clean energy and sustainable cities.