Attention-based multi-agent reinforcement learning for traffic flow stability in mountainous tunnel entrances

This study proposes a Multi-agent Fusion Double-Dueling-Deep Q-Network Traffic Flow (MF3DQN-TF) for Connected and Autonomous Vehicles (CAVs) in mountain tunnel entrance sections, considering nonlinear coupling effects. Complex road conditions and nonlinear coupling effects of tunnel exit flow often cause unstable traffic flow there, impacting traffic efficiency and safety. The new framework, combining multi - agent deep reinforcement learning and attention mechanisms, has shown marked improvements over traditional rule - based regulation methods in various traffic scenarios through comparative experiments. Actual simulations indicate it can boost traffic flow stability by over 15%, vehicle efficiency by about 20%, and cut congestion time by 18%. Specifically, it enhances average vehicle speed by 25% and reduces the traffic congestion index by 22% compared to conventional methods. The attention mechanism improves intelligent agents' decision - making efficiency, enabling real - time vehicle interaction and coordination optimization, thus enhancing intelligent transportation systems' overall adaptability. The framework helps stabilize traffic flow and ease common traffic issues at mountain tunnel entrances, strongly supporting intelligent transportation system development.