Stability Analysis of Intelligent Connected Vehicle Traffic Flow at Mountain Tunnel Entrances Using the MF3DQN-TF Framework with Attention Mechanism and Nonlinear Coupling Effects

The nonlinear coupling effect triggered by the abrupt change in light environment and complex longitudinal slope at the entrance section of mountain tunnels deteriorates the stability of intelligent and connected mixed traffic flow. Traditional control methods exhibit significant limitations in terms of dynamic environmental adaptability, data privacy protection, and cross-scenario generalization ability. Addressing the multi-physics field coupling mechanism of light-slope-vehicle, this study proposes the MF3DQN-TF intelligent control framework, achieving breakthroughs through triple innovations. Firstly, a dynamic coupling equation between light gradient attenuation and longitudinal slope resistance is established, and a six-dimensional joint state vector is constructed to quantify the intensity of the “black hole effect”. A dual-channel attention mechanism assigns a decision weight of 0.35 to environmental abrupt change factors, compressing the propagation distance of speed oscillations to within 50 meters. Secondly, a federated transfer learning architecture is designed, integrating gradient obfuscation differential privacy and low-rank tensor decomposition techniques. The communication overhead is reduced to 42MB per round, maintaining 92% control stability even in an environment with a 20% packet loss rate. Finally, a dynamic gated transfer mechanism is developed, achieving cross-scenario knowledge distillation such as in rainstorm tunnels through maximum mean discrepancy loss, reducing the target domain convergence steps by 61.6%. Simulation verification shows that this framework reduces the standard deviation of speed to 5.1 km/h, reduces high-risk events with TTC