An Operationally Interpretable Lag-Aware Directed Spatio-Temporal Graph Neural Network for Real-Time Freeway Traffic Forecasting

Abstract

Reliable short-term freeway traffic forecasting is essential for proactive traffic management, including congestion warning, ramp metering support, and traveler information services. However, many existing forecasting models treat spatial dependencies as synchronous or weakly directional, limiting their ability to represent delayed upstream–downstream traffic propagation. This study proposes Traffic-DiMAGNet, a lightweight and interpretable lag-aware directed spatio-temporal graph neural network for real-time freeway flow forecasting. The model constructs a sparse directed sensor dependency graph by integrating physical road adjacency, training-set lead–lag traffic priors, and learnable source–target node embeddings. A lag-aware bidirectional propagation module then shifts inter-sensor messages according to estimated propagation delays, while directed random-walk normalization, directional gating, and multi-scale causal convolutions preserve asymmetric traffic semantics with low computational cost. Experiments on four Caltrans PeMS datasets show that Traffic-DiMAGNet consistently outperforms recurrent, diffusion-based, attention-based, and adaptive-graph baselines regarding MAE, RMSE, and MAPE values. The learned directed lag structures provide interpretable propagation information, and the lightweight architecture supports rolling 5 min forecasting, indicating practical potential for real-time freeway monitoring and proactive traffic management.