  Accurate traffic speed prediction is an important and challenging topic for
transportation planning. Previous studies on traffic speed prediction
predominately 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 to
achieve a better prediction of traffic speed. Our incident-driven prediction
framework consists of three processes. First, we propose a critical incident
discovery method to discover urban traffic incidents with high impact on
traffic speed. Second, we design a binary classifier, which uses deep learning
methods to extract the latent incident impact features from the middle layer of
the classifier. Combining above methods, we propose a Deep Incident-Aware Graph
Convolutional Network (DIGC-Net) to effectively incorporate urban traffic
incident, spatio-temporal, periodic and context features for traffic speed
prediction. We conduct experiments on two real-world urban traffic datasets of
San Francisco and New York City. The results demonstrate the superior
performance of our model compare to the competing benchmarks.
