Determinants of traffic violations in China: A case-study with a partial proportional odds model

Abstract

Traffic crashes involving vehicles are mainly caused by illegal driving behaviors. It is of paramount importance to mitigate traffic violation occurrences. This study positions itself to characterize the effects of contributing factors on traffic violation severity. Considering different traffic violation outcomes caused by various factors, this study selects 17 factors from the spatiotemporal, road-traffic, vehicle-driver, and environment characteristics based on 55,997 valid traffic violations. A model comparison as well as the elasticity for the optimal model (partial proportional odds model) is applied to facilitate the related interpretation. The results evidenced the significant roles of time of day, vehicle type, driver age, interference, road type, weather, lighting condition, and speed limit. The findings revealed that higher-grade roads, higher speed limits, lower visibility, more interference, and increasing traffic volumes are significantly associated with a reduction in the slight probabilities but an increase in the more severe probabilities. Older drivers with more experience are correlated with a substantial increase in the slight probabilities yet an obvious decrease in the mild probabilities. The findings could provide meaningful insights to prioritize effective related countermeasures.

Keywords:

• Traffic violation severity
• contributing factors
• model comparison
• partial proportional odds model

Acknowledgements

The authors would like to acknowledge the financial support for this study provided by the National Key Research and Development Program of China (Grant No. 2019YFB1600200) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX21_0129). They are also grateful to Shangyu Public Security Bureau for the data support.

Additional information

Funding

This work was supported by National Natural Science Foundation of China.