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Assessing the Performance of Highway Safety Manual (HSM) Predictive Models for Brazilian Multilane Highways

Author

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  • Olga Beatriz Barbosa Mendes

    (Department of Transportation Engineering (EESC-USP), Sao Carlos School of Engineering, University of Sao Paulo, Sao Carlos 13566-590, Brazil)

  • Ana Paula Camargo Larocca

    (Department of Transportation Engineering (EESC-USP), Sao Carlos School of Engineering, University of Sao Paulo, Sao Carlos 13566-590, Brazil)

  • Karla Rodrigues Silva

    (Department of Transportation, RTS Administration Building, Gainesville, FL 32601, USA)

  • Ali Pirdavani

    (UHasselt, Faculty of Engineering Technology, Agoralaan, 3590 Diepenbeek, Belgium
    UHasselt, Transportation Research Institute (IMOB), Martelarenlaan 42, 3500 Hasselt, Belgium)

Abstract

This paper assesses the performance of Highway Safety Manual (HSM) predictive models when applied to Brazilian highways. The study evaluates five rural multilane highways and calculates calibration factors (C x ) of 2.62 for all types of crashes and 2.35 for Fatal or Injury (FI) crashes. The Goodness of Fit measures show that models for all types of crashes perform better than FI crashes. Additionally, the paper assesses the application of the calibrated prediction model to the atypical year of 2020, in which the COVID-19 pandemic altered traffic patterns worldwide. The HSM method was applied to 2020 using the C x obtained from the four previous years. Results show that for 2020, the observed counts were about 10% lower than the calibrated predictive model estimate of crash frequency for all types of crashes, while the calibrated prediction of FI crashes was very close to the observed counts. The findings of this study demonstrate the usefulness of HSM predictive models in identifying high-risk areas or situations and improving road safety, contributing to making investment decisions in infrastructure and road safety more sustainable.

Suggested Citation

  • Olga Beatriz Barbosa Mendes & Ana Paula Camargo Larocca & Karla Rodrigues Silva & Ali Pirdavani, 2023. "Assessing the Performance of Highway Safety Manual (HSM) Predictive Models for Brazilian Multilane Highways," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10474-:d:1185939
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Seiford, L. & Stutz, J., 1982. "A multiplicative model for efficiency analysis," Socio-Economic Planning Sciences, Elsevier, vol. 16(5), pages 223-224.
    2. Nopadon Kronprasert & Katesirint Boontan & Patipat Kanha, 2021. "Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    3. Stephen R. Barnes & Louis‐Philippe Beland & Jason Huh & Dongwoo Kim, 2022. "COVID‐19 lockdown and traffic accidents: Lessons from the pandemic," Contemporary Economic Policy, Western Economic Association International, vol. 40(2), pages 349-368, April.
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    Cited by:

    1. Muhammad Wisal Khattak & Hans De Backer & Pieter De Winne & Tom Brijs & Ali Pirdavani, 2024. "Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models," Sustainability, MDPI, vol. 16(4), pages 1-22, February.

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