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Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model

Author

Listed:
  • Hasan. A. H. Naji

    (School of Computer and Information Engineering, Nanyang Institute of Technology, Chang Jiang Road No 80, Nanyang 473004, China)

  • Qingji Xue

    (School of Computer and Information Engineering, Nanyang Institute of Technology, Chang Jiang Road No 80, Nanyang 473004, China)

  • Nengchao Lyu

    (Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China)

  • Chaozhong Wu

    (Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China)

  • Ke Zheng

    (School of Computer Science and Technology, Wuhan University of Technology, Heping Road, Wuchang District, Wuhan 430063, China)

Abstract

With the considerable increase in ownership of motor vehicles, traffic crashes have become a challenge. This paper presents a study of naturalistic driving conducted to collect driving data. The experiments were performed on different road types in the city of Wuhan in China. The collected driving data were used to develop a near-crash database, which covers driving behavior, near-crash factors, driving environment, time, demographics, and experience. A new definition of near-crash events is also proposed. The new definition considers potential risks in driving behavior, such as braking pressure, time headway, and deceleration. A clustering analysis was carried out through a K-means algorithm to classify near-crash events based on their risk level. In addition, a mixed-ordered logit model was used to examine the contributing factors associated with the driving risk of near-crash events. The results indicate that ten factors significantly affect the driving risk of near-crash events: deceleration average, vehicle kinetic energy, near-crash causes, congestion on roads, time of day, driving miles, road types, weekend, age, and experience. The findings may be used by transportation planners to understand the factors that influence driving risk and may provide valuable insights and helpful suggestions for improving transportation rules and reducing traffic collisions thus making roads safer.

Suggested Citation

  • Hasan. A. H. Naji & Qingji Xue & Nengchao Lyu & Chaozhong Wu & Ke Zheng, 2018. "Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2868-:d:163448
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    Citations

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    Cited by:

    1. Younshik Chung & Minsu Won, 2018. "A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    2. Hasan A. H. Naji & Qingji Xue & Nengchao Lyu & Xindong Duan & Tianfeng Li, 2022. "Risk Levels Classification of Near-Crashes in Naturalistic Driving Data," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    3. Lili Zheng & Yanlin Zhang & Tongqiang Ding & Fanyun Meng & Yanlin Li & Shiyu Cao, 2022. "Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks," Mathematics, MDPI, vol. 10(24), pages 1-23, December.
    4. Shenjun Yao & Jinzi Wang & Lei Fang & Jianping Wu, 2018. "Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China," Sustainability, MDPI, vol. 10(12), pages 1-11, December.
    5. Thanapong Champahom & Sajjakaj Jomnonkwao & Chinnakrit Banyong & Watanya Nambulee & Ampol Karoonsoontawong & Vatanavongs Ratanavaraha, 2021. "Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    6. S. Roderick Zhang & Bilal Farooq, 2022. "Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level," Papers 2206.09073, arXiv.org.

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