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Accurate traffic speed prediction is an important and challenging topic fortransportation planning. Previous studies on traffic speed predictionpredominately used spatio-temporal and context features for prediction.However, they have not made good use of the impact of urban traffic incidents.In this work, we aim to make use of the information of urban incidents toachieve a better prediction of traffic speed. Our incident-driven predictionframework consists of three processes. First, we propose a critical incidentdiscovery method to discover urban traffic incidents with high impact ontraffic speed. Second, we design a binary classifier, which uses deep learningmethods to extract the latent incident impact features from the middle layer ofthe classifier. Combining above methods, we propose a Deep Incident-Aware GraphConvolutional Network (DIGC-Net) to effectively incorporate urban trafficincident, spatio-temporal, periodic and context features for traffic speedprediction. We conduct experiments on two real-world urban traffic datasets ofSan Francisco and New York City. The results demonstrate the superiorperformance of our model compare to the competing benchmarks.

"How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction