Optimization of hard shoulder running on highways using multi-agent reinforcement learning considering emergency vehicles

With increasing travel demand, highway congestion and accidents have become more frequent. As an essential component of intelligent transportation systems (ITS), Hard Shoulder Running (HSR) provides a dynamic strategy to mitigate congestion by temporarily opening shoulder lanes, yet traditional methods often fail to adapt to real-time traffic changes and overlook the shoulder’s critical role in ensuring emergency vehicle access. This study proposes a novel HSR optimization framework based on the Long Short-Term Memory (LSTM) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm, integrated with an improved A* algorithm for EV lane clearing. LSTM is used to extract temporal features from traffic data to support intelligent decision-making in MADDPG, enhanced with prioritized experience replay and importance sampling. The EV lane-clearing task is formulated using graph theory, and the improved A* algorithm determines the optimal path for clearing. A simulation case was developed using Simulation of Urban MObility (SUMO) for a section of the Jinan City Ring Highway, China, evaluating four levels of traffic service. Results show the proposed method reduces total travel time by 14.6%, Time Integrated Time-to-Collision (TIT) by 45.2%, and CO2 emissions by 11.9%. Additionally, with EV intervention, braking times are reduced by up to 376.3% and travel time by 18.1% using the improved A* strategy. These findings demonstrate that the integrated LSTM-MADDPG and A* approach effectively enhances highway traffic efficiency, safety, and sustainability under complex real-world conditions.

Recommended citation: Hu, L., Tang, J., Wang, Z., Li, Z., Li, M., & Zeng, J. (2025). Optimization of hard shoulder running on highways using multi-agent reinforcement learning considering emergency vehicles. Journal of Intelligent Transportation Systems, 1-22.