IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1483-d1033643.html
   My bibliography  Save this article

Characterization of Traffic Accidents Based on Long-Horizon Aggregated and Disaggregated Data

Author

Listed:
  • Sherif Shokry

    (The Center of Road Traffic Safety, Naif Arab University for Security Sciences, Riyadh 11452, Saudi Arabia)

  • Naglaa K. Rashwan

    (Civil Engineering Department, Faculty of Engineering, Beni-Suef University, Mandated to Al Minia High Institute of Engineering and Technology, El Minia 14812, Egypt)

  • Seham Hemdan

    (Civil Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Ali Alrashidi

    (The Center of Road Traffic Safety, Naif Arab University for Security Sciences, Riyadh 11452, Saudi Arabia)

  • Amr M. Wahaballa

    (Civil Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

For sustainable transportation systems, modeling road traffic accidents is essential in order to formulate measures to reduce their harmful impacts on society. This study investigated the outcomes of using different datasets in traffic accident models with a low number of variables that can be easily manipulated by practitioners. Long-horizon aggregated and disaggregated road traffic accident datasets on Egyptian roads (for five years) were used to compare the model’s fit for different data groups. This study analyzed the results of k-means data clustering and classified the data into groups to compare the fit of the base model (Smeed’s model and different types of regression models). The results emphasized that the aggregated data used had less efficiency compared with the disaggregated data. It was found that the classification of the disaggregated dataset into reasonable groups improved the model’s fit. These findings may help in the better utilization of the available road traffic accident data for determining the best-fitting model that can assist decision-makers to choose suitable road traffic accident prevention measures.

Suggested Citation

  • Sherif Shokry & Naglaa K. Rashwan & Seham Hemdan & Ali Alrashidi & Amr M. Wahaballa, 2023. "Characterization of Traffic Accidents Based on Long-Horizon Aggregated and Disaggregated Data," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1483-:d:1033643
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1483/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1483/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Sheng Dong & Afaq Khattak & Irfan Ullah & Jibiao Zhou & Arshad Hussain, 2022. "Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations," IJERPH, MDPI, vol. 19(5), pages 1-23, March.
    2. Wachiranun Sirikul & Nida Buawangpong & Ratana Sapbamrer & Penprapa Siviroj, 2021. "Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach," IJERPH, MDPI, vol. 18(19), pages 1-14, October.
    3. Mireille Megnidio-Tchoukouegno & Jacob Adedayo Adedeji, 2023. "Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    4. 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.
    5. Syed As-Sadeq Tahfim & Yan Chen, 2022. "A Cluster-Based Approach for Analysis of Injury Severity in Interstate Crashes Involving Large Trucks," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    6. 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:15:y:2023:i:2:p:1483-:d:1033643. 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.