IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p8142-d855208.html
   My bibliography  Save this article

Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

Author

Listed:
  • Huajing Ning

    (College of Civil Engineering, Lanzhou Jiaotong University, West Anning Road #88, Lanzhou 730000, China
    School of Urban Construction and Transportation, Hefei University, Hefei Jinxiu Road #99, Hefei 230000, China)

  • Yunyan Yu

    (College of Civil Engineering, Lanzhou Jiaotong University, West Anning Road #88, Lanzhou 730000, China)

  • Lu Bai

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Si Pai Lou #2, Nanjing 210000, China
    Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong 999077, China)

Abstract

The causes of crashes on urban expressways are mostly related to the unsafe behaviors of drivers before the crash. This study focuses on sideswipe collisions on urban expressways. Through real and visual crash data, 17 unsafe behaviors were identified for the analysis of sideswipe collisions on an urban expressway. The chains of high-risk and unsafe behaviors were then revealed to investigate the relationship between drivers’ unsafe behaviors and sideswipe collisions. A Bayesian network diagram of unsafe behaviors was used to obtain the correlation between unsafe behaviors and their influence. A topology diagram of unsafe behaviors was then constructed, and relational reasoning of typical behavioral chains was conducted. Finally, the unsafe behaviors and behavior chains that were likely to cause sideswipe collisions on the urban expressway were determined. The possibility of each behavior chain was quantified through the reasoning of variable structures constructed by the Bayesian network. The result shows that the significant influential single unsafe behavior leading to sideswipe collision on urban expressways was lane change without checking the rearview mirror or not scanning the road around and queue-jumping; moreover, based on unsafe behavior chains analysis, the most influential chains leading to sideswipe collision were: improper driving behavior in an emergency—failure to turn on signal when changing lanes—distracted and inattentive driving. Some safety precautions and countermeasures aimed at unsafe behaviors could be taken before the crash. The results of the study can be used to reduce the number of sideswipe collisions, thereby improving traffic safety on urban expressways.

Suggested Citation

  • Huajing Ning & Yunyan Yu & Lu Bai, 2022. "Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network," Sustainability, MDPI, vol. 14(13), pages 1-15, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8142-:d:855208
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/8142/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/13/8142/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Lint, J.W.C. & Calvert, S.C., 2018. "A generic multi-level framework for microscopic traffic simulation—Theory and an example case in modelling driver distraction," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 63-86.
    2. Fang Zong & Hongguo Xu & Huiyong Zhang, 2013. "Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, October.
    3. Iranitalab, Amirfarrokh & Khattak, Aemal, 2020. "Probabilistic classification of hazardous materials release events in train incidents and cargo tank truck crashes," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tong Liu & Chang Wang & Rui Fu & Yong Ma & Zhuofan Liu & Tangzhi Liu, 2022. "Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles," Sustainability, MDPI, vol. 14(16), pages 1-20, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dindar, Serdar & Kaewunruen, Sakdirat & An, Min, 2022. "A hierarchical Bayesian-based model for hazard analysis of climate effect on failures of railway turnout components," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Singh, Prashant & Pasha, Junayed & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2022. "Development of exact and heuristic optimization methods for safety improvement projects at level crossings under conflicting objectives," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Li, Bokang & Afkhami, Payam & Khayamim, Razieh & Elmi, Zeinab & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2024. "A holistic optimization-based approach for sustainable selection of level crossings for closure with safety, economic, and environmental considerations," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    4. Rachel Aldred & Susana García-Herrero & Esther Anaya & Sixto Herrera & Miguel Ángel Mariscal, 2019. "Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment," IJERPH, MDPI, vol. 17(1), pages 1-16, December.
    5. Khaled Assi, 2020. "Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models," IJERPH, MDPI, vol. 17(20), pages 1-16, October.
    6. Li, Xia & You, Zhijian & Ma, Xinwei & Pang, Xiaomin & Min, Xuefeng & Cui, Hongjun, 2024. "Effect of autonomous vehicles on car-following behavior of human drivers: Analysis based on structural equation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    7. Khaled Assi & Syed Masiur Rahman & Umer Mansoor & Nedal Ratrout, 2020. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    8. Chakroborty, Partha & Pinjari, Abdul Rawoof & Meena, Jayant & Gandhi, Avinash, 2021. "A Psychophysical Ordered Response Model of Time Perception and Service Quality: Application to Level of Service Analysis at Toll Plazas," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 44-64.
    9. Ni, Ying & Li, Yixin & Yuan, Yufei & Sun, Jian, 2023. "An operational simulation framework for modelling the multi-interaction of two-wheelers on mixed-traffic road segments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    10. Mohammadian, Saeed & Zheng, Zuduo & Haque, Mazharul & Bhaskar, Ashish, 2023. "NET-RAT: Non-equilibrium traffic model based on risk allostasis theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    11. Ke Zhang & Yaming Guo, 2023. "Attention-Based Residual Dilated Network for Traffic Accident Prediction," Mathematics, MDPI, vol. 11(9), pages 1-15, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8142-:d:855208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.