IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i15p5497-d391899.html
   My bibliography  Save this article

Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol

Author

Listed:
  • Khaled Assi

    (Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Syed Masiur Rahman

    (Center for Environment and Water, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Umer Mansoor

    (Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Nedal Ratrout

    (Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily identified with little investigation on crash sites were used as an input so that the trauma center can predict the crash severity level based on the initial information provided from the crash site and prepare accordingly for the treatment of the victims. The input parameters mainly include vehicle attributes and road condition attributes. This study used the crash database of Great Britain for the years 2011–2016. A random sample of crashes representing each year was used considering the same share of severe and non-severe crashes. The models were compared based on injury severity prediction accuracy, sensitivity, precision, and harmonic mean of sensitivity and precision (i.e., F1 score). The SVM-FCM model outperformed the other developed models in terms of accuracy and F1 score in predicting the injury severity level of severe and non-severe crashes. This study concluded that the FCM clustering algorithm enhanced the prediction power of FNN and SVM models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:15:p:5497-:d:391899
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/15/5497/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/15/5497/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shengdi Chen & Shiwen Zhang & Yingying Xing & Jian Lu, 2020. "Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel," IJERPH, MDPI, vol. 17(9), pages 1-15, May.
    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.
    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. Ke Zhang & Yaming Guo, 2023. "Attention-Based Residual Dilated Network for Traffic Accident Prediction," Mathematics, MDPI, vol. 11(9), pages 1-15, April.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

    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. Al-Baraa Abdulrahman Al-Mekhlafi & Ahmad Shahrul Nizam Isha & Nicholas Chileshe & Mohammed Abdulrab & Anwar Ameen Hezam Saeed & Ahmed Farouk Kineber, 2021. "Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
    2. Mahyar Madarshahian & Aditya Balaram & Fahim Ahmed & Nathan Huynh & Chowdhury K. A. Siddiqui & Mark Ferguson, 2023. "Analysis of Injury Severity of Work Zone Truck-Involved Crashes in South Carolina for Interstates and Non-Interstates," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    3. Weiwei Qi & Shufang Zhu & Jinsong Hu, 2022. "Correlation Analysis of Real-Time Warning Factors for Construction Heavy Trucks Based on Electrified Supervision System," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    4. Younshik Chung & Jong-Jin Kim, 2023. "Exploring Factors Affecting Crash Injury Severity with Consideration of Secondary Collisions in Freeway Tunnels," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    5. 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.
    6. Chenming Jiang & Junliang He & Shengxue Zhu & Wenbo Zhang & Gen Li & Weikun Xu, 2023. "Injury-Based Surrogate Resilience Measure: Assessing the Post-Crash Traffic Resilience of the Urban Roadway Tunnels," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    7. 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.
    8. Arshad Jamal & Waleed Umer, 2020. "Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network," IJERPH, MDPI, vol. 17(20), pages 1-22, October.
    9. 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.
    10. Huiying Wen & Yingxin Du & Zheng Chen & Sheng Zhao, 2022. "Analysis of Factors Contributing to the Injury Severity of Overloaded-Truck-Related Crashes on Mountainous Highways in China," IJERPH, MDPI, vol. 19(7), pages 1-17, April.
    11. Lan Wu & Qi Shen & Gen Li, 2022. "Identifying Risk Factors for Autos and Trucks on Highway-Railroad Grade Crossings Based on Mixed Logit Model," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
    12. 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:jijerp:v:17:y:2020:i:15:p:5497-:d:391899. 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.