Integrated Deep Reinforcement Learning Agent Approach for Multi-Lane Cooperative Variable Speed Limit Control

Given the advancements in intelligent highway management systems and connected and autonomous vehicles, precise traffic flow control via highway gantries has become feasible. Bottleneck zones caused by construction or accidents, as a primary source of highway congestion and crashes, have emerged as a key focus in proactive traffic flow management. To address the issue of conflicting gantry decisions in the context of sparse gantries, multi-lane highways, and mixed traffic flows within bottleneck zones, a coordinated speed limit control model utilizing the gantry system is proposed. This model aims to enhance safety and efficiency while maintaining energy consumption levels. Building upon this coordinated control framework, an improved neural network-based Proximal Policy Optimization integrated-agent algorithm is introduced for centralized training and decision-making. The algorithm incorporates elite experience replay, enhanced exploration, entropy regularization, and gradient clipping to improve deep reinforcement learning performance and avoid local optima. Finally, simulation via SUMO and PyTorch demonstrates that the proposed control strategy improves safety metrics by 11.6%, increases space-mean speed by 6.7%, and achieves a 1.02% reduction in energy consumption compared to real-world control strategies. The effectiveness of the proposed coordinated speed limit control model and the integrated-agent training and decision-making framework is validated against traditional models and multi-agent distributed control approaches.