Work

Low-Cost vehicle rebalancing control for an autonomous mobility on demand system

Year: 2025

Type: article

Abstract: The continuous expansion of urban areas and population growth have created an urgent need for innovative solutions in traffic management. Addressing fluctuating mobility demands and optimizing resource allocation in real time are fundamental challenges for modern cities. To address these issues, this paper introduces a low computational cost mobility-on-demand (MoD) rebalancing solution designed to dynamically adapt to varying demand across the traffic network. The proposed algorithm continuously evaluates both the current state of the traffic network and projected future demand to optimize rebalancing times. It operates using two adjustable parameters: one for requesting additional vehicles and another for allowing nodes to dispatch rebalancing vehicles. Simulation results demonstrate a significant reduction in maximum waiting times compared to scenarios without rebalancing. Additionally, the proposed solution outperforms existing methods, including reinforcement learning approaches such as deep deterministic policy gradient (DDPG), and Model Predictive Control (MPC), which require significantly longer training times. This efficiency enhances operational responsiveness, making the proposed system a more practical and scalable solution for real-world urban mobility challenges.

Source: Journal of the Franklin Institute

DOI: 10.1016/j.jfranklin.2025.108333

Authors: Fernando Viadero-Monasterio, Miguel Meléndez-Useros, Hui Zhang, Beatriz L. Boada, Maria Jesus L. Boada

Institutions: Universidad Carlos III de Madrid, Beihang University

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,100