Investigating influence factors of traffic violation using multinomial logit method

Abstract

Deaths and injuries resulted from road traffic crashes remain a serious problem globally, and current trends suggest that this will continue to be the case in the foreseeable future mainly in developing countries. Among diverse cause of traffic safety challenges, traffic violation has been considered as one of the noticeable contributing factors. The main aim of the study is to identify and evaluate the major traffic violation with related risk factors using multinomial logit model. Traffic violation data of Luzhou were collected from Sichuan Province Public Security Department, China. The study result revealed six major traffic violations, including traffic light violation, illegal parking, wrong-way driving, speeding, and NOT wearing a seat belt. Urban roads classified with congested driving and severe weather conditions were the major risk factors. Among different vehicle types and use, those small car/automobile categories with private purpose use exhibit statistically significant association (p-value < 0.05) with the aforementioned traffic violations. Taking into consideration these risky contributing factors during the development of traffic regulations and enforcement will help to reduce traffic violations and create a smooth/healthy driving condition with improved traffic safety and will also increase the performance of driving in general.

Keywords:

• multinomial logistic regression
• risk factor
• traffic safety
• traffic violations

Disclosure statement

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

Additional information

Funding

The study is sponsored by the National Natural Science Foundation of China [Grant No. 71801182], China Scholarship Council (CSC) and Science & Technology Project of Sichuan Provincial Transportation Department [Grant No. 2019-D-06].