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This paper proposes a proof-of-concept AI pipeline that uses multi-modal data (images and accident reports) to assess railway crossing safety, achieving a macro F1 score of 0.757 for risk classification and an RMSE of 0.078 for safety score estimation using a fine-tuned compact VLM.
OverFlowLight is a real-time framework that prevents traffic gridlock by detecting queue overflow using multi-modal sensing and inserting dedicated overflow phases via a hybrid rule-based and RL controller. Deployed across 43 intersections, it reduces overflow incidents by 60.4% and increases network throughput by 18.2%.
Proposes a Global-Local Graph Attention Network (GLGAT) with pairwise encoding and event-based adjacency matrix for traffic forecasting, effectively capturing spatio-temporal correlations and achieving competitive performance on real-world datasets.