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

ABSTRACT

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.

KEYWORDS:

• Deep learning
• traffic prediction
• distracted driving behavior
• spatio-temporal dependence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Key R&D Project of the Ministry of Science and Technology of the People’s Republic of China [grant number 2020YFC1512004].