STAR: Spatio-Temporal Attention Rebalancing for Eco-Friendly Autonomous Mobility-on-Demand Systems

Abstract

Existing Autonomous Mobility-on-Demand (AMoD) systems and demand-responsive algorithms are designed to adaptively respond to traffic demands, enhancing traffic service quality and profitability. However, such approaches may exacerbate traffic congestion by concentrating vehicles in high-demand areas. In addition, repeated braking, acceleration, and low-speed driving in traffic jams lead to substantial increases in emissions. Moreover, existing Deep Reinforcement Learning (DRL)-based approaches fail to address the instability of cooperative learning in environments with highly variable rewards, such as carbon emissions. To address these issues, we propose the Spatio-Temporal Attention Rebalancing (STAR) framework, leveraging an encoder-decoder architecture. In the proposed framework, we maximize the number of requests served and minimize CO₂ emissions, while ensuring high service quality by limiting maximum travel delays and waiting times for passengers. To evaluate the proposed framework, we conduct simulations in realistic urban traffic scenarios. Experimental results demonstrate that our framework consistently outperforms existing baselines. In a large-scale scenario with a fleet of one hundred vehicles, our approach improves the service rate by up to 10.1% and reduces CO₂ emissions per passenger by up to 69.6% compared to the baseline methods. The proposed framework and baselines are publicly available at https://github.com/2jungeuni/eco-friendly-fleet-rebalancing.