IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i4p1680-d1593538.html
   My bibliography  Save this article

Association Rules Between Urban Road Traffic Accidents and Violations Considering Temporal and Spatial Constraints: A Case Study of Beijing

Author

Listed:
  • Hongxiao Wang

    (College of Transportation Engineering, Chang’an University, Xi’an 710000, China
    Department of Mechanical and Traffic Engineering, Ordos Insititute of Technology, Ordos 017000, China)

  • Guohua Liang

    (College of Transportation Engineering, Chang’an University, Xi’an 710000, China)

Abstract

Traffic violations are among the leading causes of accidents and significantly compromise urban road safety. This study analyzed traffic violation and incident data collected by automated enforcement systems in urban Beijing from 2019 to 2023, consisting of 3264 traffic accident records and 147,876 traffic violation records. Through a spatiotemporal data association method, 2126 violations directly associated with accidents were identified. The FP-growth algorithm was then applied to derive 18 robust association rules encompassing five categories of accidents and four categories of violations. The findings indicate that the correlation between traffic accidents and violations displays clear peak periods during the morning (8:00–9:00) and evening (17:00–18:00). Violations such as red light running, stopping beyond the stop line during a red light, and ignoring prohibitions strongly correlate with traffic accidents under specific spatiotemporal conditions. Illegally parked vehicles not only reduce road transport efficiency but also significantly elevate the risk of traffic accidents in the surrounding area. The association rules identified in this study can assist traffic managers in formulating more effective measures to mitigate traffic violations, tackle traffic accidents at their source, enhance urban traffic safety, and promote the long-term sustainability of urban transportation systems.

Suggested Citation

  • Hongxiao Wang & Guohua Liang, 2025. "Association Rules Between Urban Road Traffic Accidents and Violations Considering Temporal and Spatial Constraints: A Case Study of Beijing," Sustainability, MDPI, vol. 17(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1680-:d:1593538
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/4/1680/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/4/1680/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Youzhi Zeng & Yongkang Qiang & Ning Zhang & Xiaobao Yang & Zhenjun Zhao & Xiaoqiao Wang, 2024. "An Influencing Factors Analysis of Road Traffic Accidents Based on the Analytic Hierarchy Process and the Minimum Discrimination Information Principle," Sustainability, MDPI, vol. 16(16), pages 1-23, August.
    2. Ruan, Zhongyuan & Song, Congcong & Yang, Xu-hua & Shen, Guojiang & Liu, Zhi, 2019. "Empirical analysis of urban road traffic network: A case study in Hangzhou city, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    3. Guangnan Zhang & Ying Tan & Qiaoting Zhong & Ruwei Hu, 2021. "Analysis of Traffic Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China," IJERPH, MDPI, vol. 18(2), pages 1-11, January.
    Full references (including those not matched with items on IDEAS)

    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. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Exploring node importance evolution of weighted complex networks in urban rail transit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    2. Yamada, Takashi, 2022. "Generalizing the probability of reaching a destination in case of route blockage," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. Chen, Yuting & Mao, Jiannan & Zhang, Zhao & Huang, Hao & Lu, Weike & Yan, Qipeng & Liu, Lan, 2022. "A quasi-contagion process modeling and characteristic analysis for real-world urban traffic network congestion patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. Meng, Yangyang & Zhao, Xiaofei & Liu, Jianzhong & Qi, Qingjie & Zhou, Wei, 2023. "Data-driven complexity analysis of weighted Shenzhen Metro network based on urban massive mobility in the rush hours," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).

    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:17:y:2025:i:4:p:1680-:d:1593538. 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.