  With the rapid development of urbanization, the boom of vehicle numbers has
resulted in serious traffic accidents, which led to casualties and huge
economic losses. The ability to predict the risk of traffic accident is
important in the prevention of the occurrence of accidents and to reduce the
damages caused by accidents in a proactive way. However, traffic accident risk
prediction with high spatiotemporal resolution is difficult, mainly due to the
complex traffic environment, human behavior, and lack of real-time
traffic-related data. In this study, we collected big traffic accident data. By
analyzing the spatial and temporal patterns of traffic accident frequency, we
presented the spatiotemporal correlation of traffic accidents. Based on the
patterns we found in analysis, we proposed a high accurate deep learning model
based on recurrent neural network toward the prediction of traffic accident
risk. The predictive accident risk can be potential applied to the traffic
accident warning system. The proposed method can be integrated into an
intelligent traffic control system toward a more reasonable traffic prediction
and command organization.
