A Connected-Automated Vehicles-Based Dynamic Speed Limit Control Strategy for Improving Safety and Efficiency of Freeway Tunnels: An Augmented Lagrange Safe Reinforcement Learning Framework

The unique structure and lighting conditions of freeway tunnels significantly raise the crashe risk. As connected-automated vehicles (CAVs) increasingly coexist with human-driven vehicles in mixed traffic environments, the complexity of tunnel traffic risk increases. This study introduces a novel CAVs-based dynamic speed limit (CAVs-based DSL) control strategy specifically designed for freeway tunnels in mixed traffic to enhance safety and efficiency. This strategy utilizes CAVs as moving barriers to implement DSL control, thereby reducing the risk of crashes and improving the efficiency in tunnels. This study models the CAVs-based DSL control as a safe reinforcement learning (SRL) problem and develops the augmented Lagrange multiplier method combined with the deep deterministic policy gradient (ALM-DDPG) algorithm to solve it. A comprehensive simulation environment based on real-world tunnel scenarios is developed to evaluate the effectiveness of the proposed strategy. The results show that the ALM-DDPG-based DSL reduces traffic conflicts by 22.96%–35.68% and travel time by 15.34%–21.21%, compared to fixed speed limit control across different market penetration scenarios of CAVs, leading to significant safety and efficiency improvements. Compared with the conventional DDPG and the sum-weighted multiobjective DDPG algorithms, the ALM-DDPG algorithm yields more balanced performance in terms of traffic safety and efficiency. The ALM-DDPG algorithm also offers significant advantages over traditional Lagrange multiplier-based SRL algorithms, providing faster convergence and higher stability, especially in scenarios with high-CAV penetrations. This study highlights the significant potential of integrating SRL with CAV technology to address the complex safety challenges of freeway tunnels.