IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i20p13693-d949571.html
   My bibliography  Save this article

Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction

Author

Listed:
  • Bo Wang

    (School of Highway, Chang’an University, Xi’an 710064, China
    School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Chi Zhang

    (School of Highway, Chang’an University, Xi’an 710064, China
    Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Xi’an 710000, China)

  • Yiik Diew Wong

    (School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Lei Hou

    (School of Engineering, STEM College, RMIT University, Melbourne, VIC 3001, Australia)

  • Min Zhang

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Yujie Xiang

    (School of Highway, Chang’an University, Xi’an 710064, China)

Abstract

Road infrastructure has significant effects on road traffic safety and needs further examination. In terms of traffic crash prediction, recent studies have started to develop deep learning classification algorithms. However, given the uncertainty of traffic crashes, predicting the traffic risk potential of different road sections remains a challenge. To bridge this knowledge gap, this study investigated a real-world expressway and collected its traffic crash data between 2013 and 2020. Then, according to the time-spatial density ratio ( Pts ), road sections were assigned into three classes corresponding to low, medium, and high risk levels of traffic. Next, different classifiers were compared that were trained using the transformed and resampled feature data to construct a traffic crash risk prediction model. Last, but not least, partial dependence plots (PDPs) were employed to interpret the results and analyze the importance of individual features describing the geometry, pavement, structure, and weather conditions. The results showed that a variety of data balancing algorithms improved the performance of the classifiers, the ensemble classifier superseded the others in terms of the performance metrics, and the combined SMOTEENN and random forest algorithms improved the classification accuracy the most. In the future, the proposed traffic crash risk prediction method will be tested in more road maintenance and design safety assessment scenarios.

Suggested Citation

  • Bo Wang & Chi Zhang & Yiik Diew Wong & Lei Hou & Min Zhang & Yujie Xiang, 2022. "Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction," IJERPH, MDPI, vol. 19(20), pages 1-23, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13693-:d:949571
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/20/13693/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/20/13693/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Tianyi & Shi, Xiupeng & Wong, Yiik Diew, 2021. "A lane-changing risk profile analysis method based on time-series clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    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. Hossain, Md. Anowar & Tanimoto, Jun, 2022. "A microscopic traffic flow model for sharing information from a vehicle to vehicle by considering system time delay effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    2. Hamedi, Hamidreza & Shad, Rouzbeh & Ziaee, Seyed Ali, 2022. "A comparative study on measurement of lane-changing trajectory similarities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Dongjun Kim & Jinsung Yun & Kijung Kim & Seungil Lee, 2021. "A Comparative Study of the Robustness and Resilience of Retail Areas in Seoul, Korea before and after the COVID-19 Outbreak, Using Big Data," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    4. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.

    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:jijerp:v:19:y:2022:i:20:p:13693-:d:949571. 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.