Identification and screening of key traffic violations: based on the perspective of expressing driver’s accident risk

Abstract

Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver’s historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver’s accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers’ state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on ‘Drinking’, ‘Parking in dangerous areas’, ‘Wrong use of turn lights’, ‘Violating prohibited and restricted traffic regulations’, and ‘Disobeying prohibition sign’ will increase the probability of a driver’s subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver’s accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.

Keywords:

• Key traffic violations
• driver’s accident risk
• propensity score matching
• mutual information-based feature selection algorithm

Disclosure statement

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

Additional information

Funding

This research was jointly supported by the National Natural Science Foundation of China [72001179], International Science and Technology Innovation Cooperation Project of Science & Technology Department of Sichuan Province [2021YFH0106] and Basic Research Fund of Central University [2682021CX052].