Research Article
Optimizing Intelligent Transportation Systems with Multi-agent Reinforcement Learning: A Socio-economic Impact Assessment
@INPROCEEDINGS{10.1007/978-3-031-86370-7_13, author={Qian Cao and Jing Li and Paolo Trucco}, title={Optimizing Intelligent Transportation Systems with Multi-agent Reinforcement Learning: A Socio-economic Impact Assessment}, proceedings={Intelligent Transport Systems. 8th International Conference, INTSYS 2024, Pisa, Italy, December 5--6, 2024, Revised Selected Papers}, proceedings_a={INTSYS}, year={2025}, month={4}, keywords={Traffic Optimization Multi-Agent Systems Socio-Economic Impact}, doi={10.1007/978-3-031-86370-7_13} }- Qian Cao
Jing Li
Paolo Trucco
Year: 2025
Optimizing Intelligent Transportation Systems with Multi-agent Reinforcement Learning: A Socio-economic Impact Assessment
INTSYS
Springer
DOI: 10.1007/978-3-031-86370-7_13
Abstract
Rapid urbanization has exacerbated traffic congestion, presenting significant socio-economic and environmental challenges globally. This paper evaluates the socio-economic impact of implementing Intelligent Transportation Systems (ITS) enhanced by a novel Socio-Economic Reinforcement Learning (SERL) framework. We aim to minimize congestion and enhance overall transportation efficiency. The proposed method employs a hierarchical reinforcement learning algorithm specifically designed for complex multi-intersection urban traffic networks, considering socio-economic and environmental factors. Extensive simulations utilizing real-world traffic data assess the impact on travel time, fuel consumption, and emission levels. Experimental results indicate that our approach reduces average travel time by up to 26.67% compared to fixed-time control methods, decreases fuel consumption by 13.99%, and lowers CO(x)/NO(x)emissions by 20.82% in specific scenarios. These significant improvements over traditional and existing RL-based methods underscore the potential of SERL-powered ITS in promoting sustainable urban development and improving socio-economic outcomes.
