Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models.

Journal: Journal of safety research
PMID: 39986873

Abstract

INTRODUCTION: Transportation companies have increasingly shifted their workforce from permanent to outsourced roles, a trend that has consequences for self-employed truck drivers. This transition leads to extended working hours, resulting in fatigue and an increased risk of crashes. The present study investigates the factors contributing to fatigue and impairment in truck driving performance while developing a machine learning-based model for predicting the risk of traffic crashes.

Authors

  • Rodrigo Duarte Soliani
    Federal Institute of Acre, Av. Brazil, 920 - ZIP Code: 69.903-06, Rio Branco/AC, Brazil.
  • Alisson Vinicius Brito Lopes
    Federal Institute of Acre, Av. Brazil, 920 - ZIP Code: 69.903-06, Rio Branco/AC, Brazil.
  • Fábio Santiago
    Aeronautics Institute of Technology, Praça Marechal Eduardo Gomes, 50 - Zip Code: 12228-900, São José dos Campos/SP, Brazil.
  • Luiz Bueno da Silva
    Federal University of Paraíba, Via Expressa Padre Zé, s/n - Zip Code: 58051-970, João Pessoa PB Brazil.
  • Nwabueze Emekwuru
    Engineering Department, Harper Adams University, Newport TF10 8NB, United Kingdom. Electronic address: eemekwuru@harper-adams.ac.uk.
  • Ana Carolina Lorena
    Aeronautics Institute of Technology, Praça Marechal Eduardo Gomes, 50 - Zip Code: 12228-900, São José dos Campos/SP, Brazil.

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