⚡ Quick Summary
This study evaluated the risk of crashes among self-employed truck drivers in São Paulo, Brazil, highlighting the impact of fatigue on driving performance. Utilizing machine learning algorithms, the research achieved accuracy rates between 78% and 85% in predicting crash likelihood.
🔍 Key Details
- 📊 Participants: 363 self-employed truck drivers
- 🧩 Key Findings: 63% were smokers; 50% reported drug use
- ⏰ Driving Hours: Average of 14.62 hours before fatigue
- 💤 Sleep Duration: Average of 5.92 hours in the last 24 hours
- ⚙️ Technology: Eight machine learning algorithms employed
- 🏆 Performance: Accuracy rates between 78% and 85%
🔑 Key Takeaways
- 🚚 Self-employed truck drivers face significant fatigue-related risks.
- 💡 Machine learning can effectively predict crash risks based on fatigue data.
- 📉 High prevalence of smoking and drug use among participants.
- ⏳ Long driving hours contribute to fatigue and increased crash risk.
- 📊 Accurate models can inform safety policies for truck drivers.
- 🌍 Study conducted in São Paulo, Brazil, highlighting regional issues.
- 🛠️ Practical applications can enhance driver safety and public well-being.
📚 Background
The shift from permanent to outsourced roles in transportation has raised concerns regarding the safety of self-employed truck drivers. This transition often leads to extended working hours, which can result in fatigue and an increased risk of traffic crashes. Understanding the factors contributing to fatigue is crucial for developing effective safety measures.
🗒️ Study
The study involved a comprehensive questionnaire administered to 363 self-employed truck drivers in São Paulo, Brazil. The questionnaire assessed various aspects, including sociodemographic characteristics, health, sleep patterns, and working conditions. The findings revealed alarming statistics regarding smoking, alcohol consumption, and drug use among the participants.
📈 Results
Participants reported driving for an average of 14.62 hours before feeling fatigued, with only 5.92 hours of sleep in the previous 24 hours. The study’s machine learning models demonstrated impressive accuracy rates, ranging from 78% to 85%, in predicting the likelihood of crashes based on fatigue data.
🌍 Impact and Implications
The implications of this study are significant for both self-employed truck drivers and the general public. By leveraging machine learning to predict crash risks, transportation companies can implement targeted interventions to enhance driver safety. These findings can inform policies aimed at reducing fatigue-related incidents, ultimately improving road safety for everyone.
🔮 Conclusion
This research underscores the critical role of fatigue management in ensuring the safety of self-employed truck drivers. The successful application of machine learning models to predict crash risks represents a promising advancement in transportation safety. Continued research and policy development are essential to address these challenges effectively.
💬 Your comments
What are your thoughts on the findings of this study? How can we better support self-employed truck drivers in managing fatigue? 💬 Share your insights in the comments below or connect with us on social media:
Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models.
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.
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 São 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.
Author: [‘Duarte Soliani R’, ‘Vinicius Brito Lopes A’, ‘Santiago F’, ‘da Silva LB’, ‘Emekwuru N’, ‘Carolina Lorena A’]
Journal: J Safety Res
Citation: Duarte Soliani R, et al. Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models. Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models. 2025; 92:68-80. doi: 10.1016/j.jsr.2024.11.002