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

Aug 5, 2026

Transportation companies increasingly shift labor to outsourced/self-employed arrangements, often extending work schedules for self-employed truck drivers and amplifying fatigue and crash risk. The study examines contributors to fatigue and impaired driving performance while building a machine learning model to predict traffic crash likelihood. A questionnaire covering sociodemographics, health, sleep, and working conditions was administered to 363 drivers in São Paulo, Brazil. Results report high smoking and substance use, typical fatigue onset after 14.62 h of driving, and sleep of 5.92 h in the prior 24 h. Waiting times for loading/unloading significantly affect duty duration and rest; eight algorithms achieved 78%–85% accuracy, supporting practical safety and well-being interventions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Outsourcing and extended working schedules

Fatigue and driving safety challenges

Economic pressures and deteriorating occupational conditions

Method

Questionnaire design and participant recruitment

Data collected: sociodemographics, health, sleep, working conditions

Results

Smoking, alcohol, and drug use rates

Time to fatigue and sleep duration

Impact of loading/unloading waiting times

Machine learning prediction performance

Conclusions and Practical Applications

Validated machine learning models for crash-risk prediction

Implications for policies and driver safety

Risk of crashes among selfemployed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models by Duarte Soliani, R. , Vinicius Brito Lopes, A. , Santiago, F. , da Silva, L. B. , Emekwuru, N. and Carolina Lorena, A. Copyright, publisher and additional information: Publishers’ version distributed under the terms of the Creative Commons Attribution License DOI link to the version of record on the publisher’s site Journal of Safety Research 92 (2025) 68–80 Contents lists available at ScienceDirect Journal of Safety Research journal [homepage:](homepage: www.elsevier.com/locate/jsr)[ www.elsevier.com/locate/jsr](homepage: www.elsevier.com/locate/jsr) | Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models | | | | | --- | --- | --- | --- | | Rodrigo Duarte Soliania, Alisson Vinicius Brito Lopes a, F´abio Santiago b, Luiz Bueno da Silva c, Nwabueze Emekwurud,*, Ana Carolina Lorenab
a Federal Institute of Acre, Av. Brazil, 920-ZIP Code: 69.903-06, Rio Branco/AC, Brazil
b Aeronautics Institute of Technology, Praça Marechal Eduardo Gomes, 50-Zip Code: 12228-900, S˜ao Jos´e dos Campos/SP, Brazil c Federal University of Paraíba, Via Expressa Padre Z´e, s/n – Zip Code: 58051-970, Jo˜ao Pessoa PB Brazil
d Engineering Department, Harper Adams University, Newport TF10 8NB, United Kingdom | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Self-employed truck drivers Fatigue
Occupational conditions Sleep
Working hours | | 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. Method: To achieve this, a comprehensive questionnaire was designed, covering various aspects of the participants’ sociodemographic characteristics, health, sleep, and working conditions. The questionnaire was administered to 363 self-employed truck drivers operating in the State of S˜ao Paulo, Brazil. Approximately 63% of the participants were smokers, while 17.56% reported drinking alcohol more than four times a week, and also admitted to being involved in at least one crash in the last three years. Fifty percent of the respondents reported consuming drugs (such as amphetamines, marijuana, or cocaine). Results: The surveyed individuals declared driving for approximately 14.62 h (SD = 1.97) before they felt fatigued, with an average of approximately 5.92 h of sleep in the last 24 h (SD = 0.96). Truck drivers unanimously agreed that waiting times for truck loading/unloading significantly impact the duration of their working day and rest time. The study employed eight machine learning algorithms to estimate the likelihood of truck drivers being involved in crashes, achieving accuracy rates ranging between 78% and 85%. Conclusions: These results validated the construction of accurate machine learning-derived models. Practical Applications: These findings can inform policies and practices aimed at enhancing the safety and well-being of self-employed truck drivers and the broader public. | | 1. Introduction In the transportation industry, there is an ongoing trend of shifting workers from permanent employment to outsourced or self-employed arrangements to reduce overall costs (Messias et al., 2019). By hiring self-employed drivers, transportation companies can lower operating expenses related to fuel, vehicle maintenance, and tires, as well as the responsibilities and costs associated with labor charges (Rocha et al., 2018). However, this model often results in drivers facing mor

FAQs

What occupational trend motivates this study on self-employed truck drivers?

Transportation companies increasingly shift from permanent employment to outsourced/self-employed arrangements to reduce costs, which can lead to longer work schedules and higher fatigue-related crash risk.

How was fatigue and driving-risk information collected for modeling?

A comprehensive questionnaire gathered participants’ sociodemographic characteristics, health, sleep patterns, and working conditions. It was administered to 363 self-employed truck drivers in São Paulo, Brazil.

Which factors and metrics showed links to crash risk in the findings?

Drivers reported fatigue onset after about 14.62 hours of driving and average sleep of about 5.92 hours in the prior 24 hours. Waiting times for truck loading/unloading significantly influenced work duration and rest time, and machine learning models using these data achieved 78%–85% accuracy.

Aug 5, 2026

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