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Older Pedestrian Traffic Crashes Severity Analysis Based on an Emerging Machine Learning XGBoost

Author

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  • Manze Guo

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
    School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Zhenzhou Yuan

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
    School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Bruce Janson

    (Department of Civil Engineering, University of Colorado Denver, Denver, CO 80217-3364, USA)

  • Yongxin Peng

    (Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-3135, USA)

  • Yang Yang

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
    School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
    Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA)

  • Wencheng Wang

    (Beijing Municipal Institute of City Planning & Design, Beijing 100045, China)

Abstract

Older pedestrians are vulnerable on the streets and at significant risk of injury or death when involved in crashes. Pedestrians’ safety is critical for roadway agencies to consider and improve, especially older pedestrians aged greater than 65 years old. To better protect the older pedestrian group, the factors that contribute to the older crashes need to be analyzed deeply. Traditional modeling approaches such as Logistic models for data analysis may lead to modeling distortions due to the independence assumptions. In this study, Extreme Gradient Boosting (XGBoost), is used to model the classification problem of three different levels of severity of older pedestrian traffic crashes from crash data in Colorado, US. Further, Shapley Additive explanations (SHAP) are implemented to interpret the XGBoost model result and analyze each feature’s importance related to the levels of older pedestrian crashes. The interpretation results show that the driver characteristic, older pedestrian characteristics, and vehicle movement are the most important factors influencing the probability of the three different severity levels. Those results investigate each severity level’s correlation factors, which can inform the department of traffic management and the department of road infrastructure to protect older pedestrians by controlling or managing some of those significant features.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:926-:d:482269
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    References listed on IDEAS

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    1. Chia-Yuan Yu, 2015. "How Differences in Roadways Affect School Travel Safety," Journal of the American Planning Association, Taylor & Francis Journals, vol. 81(3), pages 203-220, July.
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    Citations

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    Cited by:

    1. Yang, Yang & He, Kun & Wang, Yun-peng & Yuan, Zhen-zhou & Yin, Yong-hao & Guo, Man-ze, 2022. "Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    2. Weijia (Vivian) Li & Kara M. Kockelman, 2022. "How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE," Growth and Change, Wiley Blackwell, vol. 53(1), pages 342-376, March.
    3. Maciej Kruszyna & Marta Matczuk-Pisarek, 2021. "The Effectiveness of Selected Devices to Reduce the Speed of Vehicles on Pedestrian Crossings," Sustainability, MDPI, vol. 13(17), pages 1-21, August.
    4. Lei Yang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Belgacem Bouallegue & Muhammad Faisal Javed & Nermin M. Salem, 2022. "Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestri," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    5. Piotr Szagała & Piotr Olszewski & Witold Czajewski & Paweł Dąbkowski, 2021. "Active Signage of Pedestrian Crossings as a Tool in Road Safety Management," Sustainability, MDPI, vol. 13(16), pages 1-13, August.
    6. Xiangning Dong & Xuhao Zhu & Minghua Hu & Jie Bao, 2023. "A Methodology for Predicting Ground Delay Program Incidence through Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    7. 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.
    8. Wenlong Tao & Mahdi Aghaabbasi & Mujahid Ali & Abdulrazak H. Almaliki & Rosilawati Zainol & Abdulrhman A. Almaliki & Enas E. Hussein, 2022. "An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    9. Shengxue Zhu & Ke Wang & Chongyi Li, 2021. "Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach," IJERPH, MDPI, vol. 18(21), pages 1-20, November.

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