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Parametric and Non-Parametric Analyses for Pedestrian Crash Severity Prediction in Great Britain

Author

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  • Maria Rella Riccardi

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy)

  • Filomena Mauriello

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy)

  • Sobhan Sarkar

    (Information Systems & Business Analytics, Indian Institute of Management Ranchi, Ranchi 834 008, India)

  • Francesco Galante

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy)

  • Antonella Scarano

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy)

  • Alfonso Montella

    (Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy)

Abstract

The study aims to investigate the factors that are associated with fatal and severe vehicle–pedestrian crashes in Great Britain by developing four parametric models and five non-parametric tools to predict the crash severity. Even though the models have already been applied to model the pedestrian injury severity, a comparative analysis to assess the predictive power of such modeling techniques is limited. Hence, this study contributes to the road safety literature by comparing the models by their capabilities of identifying the significant explanatory variables, and by their performances in terms of the F-measure, the G-mean, and the area under curve. The analyses were carried out using data that refer to the vehicle–pedestrian crashes that occurred in the period of 2016–2018. The parametric models confirm their advantages in offering easy-to-interpret outputs and understandable relations between the dependent and independent variables, whereas the non-parametric tools exhibited higher classification accuracies, identified more explanatory variables, and provided insights into the interdependencies among the factors. The study results suggest that the combined use of parametric and non-parametric methods may effectively overcome the limits of each group of methods, with satisfactory prediction accuracies and the interpretation of the factors contributing to fatal and serious crashes. In the conclusion, several engineering, social, and management pedestrian safety countermeasures are recommended.

Suggested Citation

  • Maria Rella Riccardi & Filomena Mauriello & Sobhan Sarkar & Francesco Galante & Antonella Scarano & Alfonso Montella, 2022. "Parametric and Non-Parametric Analyses for Pedestrian Crash Severity Prediction in Great Britain," Sustainability, MDPI, vol. 14(6), pages 1-44, March.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3188-:d:766810
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    References listed on IDEAS

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    1. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    2. Greene,William H. & Hensher,David A., 2010. "Modeling Ordered Choices," Cambridge Books, Cambridge University Press, number 9780521194204, September.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    4. Abay, Kibrom A., 2013. "Examining pedestrian-injury severity using alternative disaggregate models," Research in Transportation Economics, Elsevier, vol. 43(1), pages 123-136.
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    Cited by:

    1. 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.
    2. Mostafa Sharafeldin & Ahmed Farid & Khaled Ksaibati, 2022. "Injury Severity Analysis of Rear-End Crashes at Signalized Intersections," Sustainability, MDPI, vol. 14(21), pages 1-14, October.

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