Multi-intersection traffic signal control based on dynamic spatiotemporal memory enhanced learning

Multi-intersection traffic signal control based on dynamic spatiotemporal memory enhanced learning

In multi-intersection traffic signal control, spatial information contains rich traffic state features, including intersection topology and lane associations. Effectively extracting and integrating this spatial information is crucial for accurately characterizing the evolution of traffic state. However, most existing methods rely on static graph structures and, therefore, cannot dynamically model the spatiotemporal correlations of traffic states, limiting their adaptability to real-time traffic scenarios. To address these limitations, we propose a multi-intersection traffic signal control method based on dynamic spatiotemporal memory enhanced learning (DSMEL). First, we develop an adaptive update mechanism for dynamic heterogeneous graphs that analyzes correlations among heterogeneous traffic features in real time to generate adaptive representations of spatial relationships. Second, we introduce a dual-memory enhancement model based on spatiotemporal decoupling that uses a multi-head attention mechanism to process spatial and temporal features separately. Specifically, we design a temporal memory module to model periodic temporal dynamics within the road network and a spatial memory module to track the evolving topological relationships among traffic nodes. This enables a fine-grained capture of dynamic spatiotemporal features within the road network. Finally, we propose an adaptive weight learning method based on double entropy regularization that incorporates online learning of dynamic game weight matrices and integrates entropy-constrained policy optimization with novel reward and loss functions to enhance system stability and promote optimal convergence in multi-agent coordination. Extensive experiments on synthetic and real-world scenarios show that, compared with baseline methods, DSMEL reduces queue length by 27.19% to 49.89%, occupancy rate by 10.93% to 43.94%, and vehicle count by 11.39% to 43.68%. Furthermore, DSMEL demonstrated superior performance over both traditional traffic signal control methods and reinforcement learning-based approaches in extreme traffic scenarios, reducing the average queue length by 21.23% and the maximum queue length by 18.20%.