A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction

Abstract

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. Based on the quantitative analysis of big traffic accident data, this paper first introduced an important characteristic of traffic accidents - the spatiotemporal correlation, and then constructed a high accurate deep learning model for traffic accident risk prediction based on spatiotemporal correlation pattern. 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.

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