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Association between Crash Attributes and Drivers’ Crash Involvement: A Study Based on Police-Reported Crash Data

Author

Listed:
  • Guofa Li

    (Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Weijian Lai

    (Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Xingda Qu

    (Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

Abstract

Understanding the association between crash attributes and drivers’ crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers’ crash involvement in different types of crashes. Results showed that drivers’ involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.

Suggested Citation

  • Guofa Li & Weijian Lai & Xingda Qu, 2020. "Association between Crash Attributes and Drivers’ Crash Involvement: A Study Based on Police-Reported Crash Data," IJERPH, MDPI, vol. 17(23), pages 1-16, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:9020-:d:455773
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    References listed on IDEAS

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    1. Yuhui Zhao & Xinyan Zhu & Wei Guo & Bing She & Han Yue & Ming Li, 2019. "Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    2. Fangrong Chang & Maosheng Li & Pengpeng Xu & Hanchu Zhou & Md. Mazharul Haque & Helai Huang, 2016. "Injury Severity of Motorcycle Riders Involved in Traffic Crashes in Hunan, China: A Mixed Ordered Logit Approach," IJERPH, MDPI, vol. 13(7), pages 1-15, July.
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    Cited by:

    1. Shuaiming Chen & Haipeng Shao & Ximing Ji, 2021. "Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach," IJERPH, MDPI, vol. 18(23), pages 1-20, December.

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