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Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques

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  • Mubarak Alrumaidhi

    (Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA 24061, USA
    Civil Engineering Department, College of Technological Studies, Public Authority for Applied Education and Training, Shuwaikh 70654, Kuwait)

  • Mohamed M. G. Farag

    (Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA 24061, USA
    College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria 1029, Egypt)

  • Hesham A. Rakha

    (Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA 24061, USA
    Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, USA)

Abstract

As the global elderly population continues to rise, the risk of severe crashes among elderly drivers has become a pressing concern. This study presents a comprehensive examination of crash severity among this demographic, employing machine learning models and data gathered from Virginia, United States of America, between 2014 and 2021. The analysis integrates parametric models, namely logistic regression and linear discriminant analysis (LDA), as well as non-parametric models like random forest (RF) and extreme gradient boosting (XGBoost). Central to this study is the application of resampling techniques, specifically, random over-sampling examples (ROSE) and the synthetic minority over-sampling technique (SMOTE), to address the dataset’s inherent imbalance and enhance the models’ predictive performance. Our findings reveal that the inclusion of these resampling techniques significantly improves the predictive power of parametric models, notably increasing the true positive rate for severe crash prediction from 6% to 60% and boosting the geometric mean from 25% to 69% in logistic regression. Likewise, employing SMOTE resulted in a notable improvement in the non-parametric models’ performance, leading to a true positive rate increase from 8% to 36% in XGBoost. Moreover, the study established the superiority of parametric models over non-parametric counterparts when balanced resampling techniques are utilized. Beyond predictive modeling, the study delves into the effects of various contributing factors on crash severity, enhancing the understanding of how these factors influence elderly road safety. Ultimately, these findings underscore the immense potential of machine learning models in analyzing complex crash data, pinpointing factors that heighten crash severity, and informing targeted interventions to mitigate the risks of elderly driving.

Suggested Citation

  • Mubarak Alrumaidhi & Mohamed M. G. Farag & Hesham A. Rakha, 2023. "Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques," Sustainability, MDPI, vol. 15(13), pages 1-30, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9878-:d:1176070
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    References listed on IDEAS

    as
    1. Seunghoon Kim & Youngbin Lym & Ki-Jung Kim, 2021. "Developing Crash Severity Model Handling Class Imbalance and Implementing Ordered Nature: Focusing on Elderly Drivers," IJERPH, MDPI, vol. 18(4), pages 1-23, February.
    2. Manze Guo & Zhenzhou Yuan & Bruce Janson & Yongxin Peng & Yang Yang & Wencheng Wang, 2021. "Older Pedestrian Traffic Crashes Severity Analysis Based on an Emerging Machine Learning XGBoost," Sustainability, MDPI, vol. 13(2), pages 1-26, January.
    3. Fahad M. Almasoudi, 2023. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    4. Neda Abdelhamid & Arun Padmavathy & David Peebles & Fadi Thabtah & Daymond Goulder-Horobin, 2020. "Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.
    5. Mubarak Alrumaidhi & Hesham A. Rakha, 2022. "Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach," Sustainability, MDPI, vol. 14(18), pages 1-12, September.
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