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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.

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Optimizing Intelligent Transportation Systems with Multi-agent Reinforcement Learning: A Socio-economic Impact Assessment

  • 会议论文

  • 发表日期：

    2025年

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  • Qian Cao,
  • Jing Li,
  • Paolo Trucco

摘要

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.