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Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods

Author

Listed:
  • Giovanny Pillajo-Quijia

    (University Institute of Automobile Research Francisco Aparicio Izquierdo (INSIA), Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain)

  • Blanca Arenas-Ramírez

    (University Institute of Automobile Research Francisco Aparicio Izquierdo (INSIA), Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain)

  • Camino González-Fernández

    (Statistical Laboratory, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain)

  • Francisco Aparicio-Izquierdo

    (University Institute of Automobile Research Francisco Aparicio Izquierdo (INSIA), Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain)

Abstract

The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.

Suggested Citation

  • Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1324-:d:319482
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    References listed on IDEAS

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    1. Edmond L. Toy & James K. Hammitt, 2003. "Safety Impacts of SUVs, Vans, and Pickup Trucks in Two‐Vehicle Crashes," Risk Analysis, John Wiley & Sons, vol. 23(4), pages 641-650, August.
    2. Bei Zhou & Xinfen Zhang & Shengrui Zhang & Zongzhi Li & Xin Liu, 2019. "Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity," Sustainability, MDPI, vol. 11(12), pages 1-15, June.
    3. Lucian-Ionel Cioca & Larisa Ivascu, 2017. "Risk Indicators and Road Accident Analysis for the Period 2012–2016," Sustainability, MDPI, vol. 9(9), pages 1-15, August.
    4. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    5. Dadashova, Bahar & Ramírez Arenas, Blanca & McWilliams Mira, José & Izquierdo Aparicio, Francisco, 2014. "Explanatory and prediction power of two macro models. An application to van-involved accidents in Spain," Transport Policy, Elsevier, vol. 32(C), pages 203-217.
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    Citations

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    Cited by:

    1. Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Fabricio Esteban Espinoza-Molina & Christian Fernando Ojeda-Romero & Henry David Zumba-Paucar & Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Francisco Aparicio-Izquierdo, 2021. "Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    3. Maria Rodionova & Angi Skhvediani & Tatiana Kudryavtseva, 2022. "Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    4. Jaeheon Choi & Kyuil Lee & Hyunmyung Kim & Sunghi An & Daisik Nam, 2020. "Classification of Inter-Urban Highway Drivers’ Resting Behavior for Advanced Driver-Assistance System Technologies using Vehicle Trajectory Data from Car Navigation Systems," Sustainability, MDPI, vol. 12(15), pages 1-20, July.

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