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

• A low-cost MoD rebalancing solution adapts to real-time demand. • The algorithm optimizes rebalancing using two adjustable parameters. • Reduced training time and outperforms deep reinforcement learning and MPC. 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.