A hybrid deep learning model trained on SUMO-generated rear-end and intersection crash scenarios produces multi-horizon forecasts of traffic incidents and congestion on the Broadway corridor.
History-based road traffic anomaly detection using deep learning and real-world data
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Spatiotemporal Forecasting of Incidents and Congestion with Implications for Sustainable Traffic Control
A hybrid deep learning model trained on SUMO-generated rear-end and intersection crash scenarios produces multi-horizon forecasts of traffic incidents and congestion on the Broadway corridor.