Optimisation of Lane‐Level Dynamic Traffic Control Strategy Based on Bidirectional Adaptive Gated Graph Convolutional Network and Deep Reinforcement Learning

ABSTRACT This paper innovatively proposes a lane‐level dynamic traffic control strategy optimisation method integrating the bidirectional adaptive gated graph convolutional network (Bi‐AGGCN) and deep reinforcement learning (DRL). The core innovation lies in three aspects. First, Bi‐AGGCN is introduced to precisely capture the spatiotemporal dependencies of traffic flow by simultaneously considering forward and backward information, overcoming the limitations of traditional unidirectional models. Second, an improved deep Q‐network (DQN) algorithm is adopted, incorporating a dual network structure, experience pool sampling strategy, and dominance function, which effectively enhances the learning speed and estimation accuracy of the value function. Third, the combination of Bi‐AGGCN and DRL enables the framework to automatically adjust traffic signal parameters based on real‐time traffic flow, achieving dynamic optimisation of traffic flow. Experimental results indicate that compared with traditional timed signal control (FTSC), fast Q‐learning (FQ learning), and modified DQN (M‐DQN) algorithms, the proposed Bi‐AGGCN + DRL model demonstrates significant advantages. In the experiment, the traffic flow of this model reaches 2600 pcu/h, the delay time is reduced to 90 s, the lane‐level control response speed is shortened to 5 s, and the average lane speed is increased to 110 km/h. This verifies the efficiency and feasibility of the model in lane‐level traffic control, providing feasible technical support and optimisation directions for the traffic management of actual highways.