IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i20p11198-d653514.html
   My bibliography  Save this article

Urban Road Accident Black Spot Identification and Classification Approach: A Novel Grey Verhuls–Empirical Bayesian Combination Method

Author

Listed:
  • Yan Wan

    (School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China)

  • Wenqiang He

    (School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China)

  • Jibiao Zhou

    (School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China
    College of Transportation Engineering, Tongji University, Shanghai 200092, China)

Abstract

The identification and classification of accident black spots on urban roads is a key element of road safety research. To solve the problems caused by the randomness of accident occurrences and the unclear classification of accident black spots by the traditional model, we propose a method that can quickly identify and classify accident black spots on urban roads: a combined grey Verhuls–Empirical Bayesian method. The grey Verhuls model is used to obtain the predicted/expected numbers of accidents at accident hazard locations, and the empirical Bayesian approach is used to derive two accident black spot discriminators, a safety improvement space and a safety index (SI), and to classify the black spots into two, three, four and five levels according to the range of the SI. Finally, we validate this combined method on examples. High-quality and high-accuracy data are obtained from the accident collection records of the Ningbo Jiangbei District from March to December 2020, accounting for 90.55% of the actual police incidents during this period. The results show that the combined grey Verhuls–Empirical Bayesian method can identify accident black spots quickly and accurately due to the consideration of accident information from the same types of accident locations. The accident black point classification results show that the five-level rating of accident black points is most reasonable. Our study provides a new idea for accident black spot identification and a feasible method for accident black spot risk level classification.

Suggested Citation

  • Yan Wan & Wenqiang He & Jibiao Zhou, 2021. "Urban Road Accident Black Spot Identification and Classification Approach: A Novel Grey Verhuls–Empirical Bayesian Combination Method," Sustainability, MDPI, vol. 13(20), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11198-:d:653514
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/20/11198/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/20/11198/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zeng, Qiang & Wen, Huiying & Huang, Helai & Wang, Jie & Lee, Jinwoo, 2020. "Analysis of crash frequency using a Bayesian underreporting count model with spatial correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(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. Ioannis Karamanlis & Andreas Nikiforiadis & George Botzoris & Alexandros Kokkalis & Socrates Basbas, 2023. "Towards Sustainable Transportation: The Role of Black Spot Analysis in Improving Road Safety," Sustainability, MDPI, vol. 15(19), pages 1-12, October.
    2. Rishuang Sun & Chi Zhang & Yujie Xiang & Lei Hou & Bo Wang, 2022. "Identification Method for Crash-Prone Sections of Mountain Highway under Complex Weather Conditions," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    3. Bakhtiar Feizizadeh & Davoud Omarzadeh & Ayyoob Sharifi & Abolfazl Rahmani & Tobia Lakes & Thomas Blaschke, 2022. "A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(12), pages 1-20, June.

    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. Zhenggan Cai & Fulu Wei & Zhenyu Wang & Yongqing Guo & Long Chen & Xin Li, 2021. "Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation," Sustainability, MDPI, vol. 13(13), pages 1-24, July.

    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:13:y:2021:i:20:p:11198-:d:653514. 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.