A Simulation Study of ST-GNN-Based Risk Warning and Active Visual Guidance for Low-Position Lighting on Bridge Segments
Fang Cao, Yaokun Ku, Xiaoxue Cao, Zhitao Chen, Gang Li, Wenyan Han, Bailiang Lu, Zhi ChenTraffic risks on bridge segments exhibit spatiotemporal dependence under congestion, low visibility, and sudden traffic incidents. This study develops and evaluates a reproducible simulation framework that links short-term risk-proxy forecasting with graded low-position lighting control for a 1200 m linear bridge corridor. The framework uses six traffic and environmental scenario families, a direction-separated graph convolution and gated recurrent unit model, explicit baselines and graph ablations, and a rule-based upstream coordination controller. The model predicts a continuous conflict-exposure proxy for 20 control zones over a five-minute horizon. Across ten training seeds, the proposed DS-GCN-GRU achieved a mean RMSE of 0.1094 ± 0.0004, MAE of 0.0773 ± 0.0007, macro-F1 of 0.8339 ± 0.0082, Level 3 recall of 0.8777 ± 0.0095, and high-risk PR-AUC of 0.9292 ± 0.0010. Real-world safety benefits and driver responses to the proposed lighting commands have not been validated.