A hybrid deep learning method for distracted driving risk prediction based on spatio-temporal driving behavior data

Timely exact distracted driving risk prediction is beneficial to perceive real-time traffic risk, which is an essential but challenging task in modern traffic safety management. The use and improvement of measures for road safety management will be better facilitated by grasping and analyzing the spatio-temporal patterns of driving behavior and forming predictions. In this paper, a Distracted Driving Risk Prediction (DDRP) neural network by deep learning and spatio-temporal dependence is proposed, which to accurately predict the scale of distracted driving behavior on road networks. Then, the method is employed for distracted driving risk prediction based on the provincial road network. The experiment demonstrates that our method performs relatively better than the other methods applied in this paper. In addition, the method can adapt to predict the scale of distracted driving behavior in different categories, time intervals, and grid cells.