Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers
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Summary
This study addresses the critical issue of driver fatigue in commercial vehicle operations, aiming to demonstrate the implementation of a new fatigue identification model. While previous research has relied on driving simulators, intrusive physiological sensors, or post-accident analysis, this paper proposes a non-intrusive, proactive approach based on the analysis of attitudes and self-reported behavior. The primary motivation is to provide transport companies with a tool to identify "weakest links" in their safety protocols, rank companies by safety performance, and implement targeted measures to prevent accidents caused by drowsiness. The methodology involved a survey conducted between April and July 2018 across five transport companies in Serbia: three engaged in passenger transport and two in goods transport. A total of 265 drivers participated, including 84 truck drivers. The researchers applied a face-to-face model where drivers selected subcategories for 11 relevant indicators influencing fatigue, such as sleep quality, amount of sleep, driving time, and daily rest. These indicators were assigned weight coefficients based on expert judgment, with sleep quality identified as the most influential factor. The model processed this data to determine whether a driver was fatigued before the start of their shift, without requiring additional hardware like cameras or electrodes. The results indicated that 16.6% of the surveyed drivers were identified as fatigued prior to starting their shifts. The analysis revealed distinct patterns between passenger and goods transport companies. Passenger transport company TC3 had the highest percentage of fatigued drivers (35%), while goods transport companies showed lower rates (17%). In passenger transport company TC1, the primary safety issue was the excessive violation of legal driving time limits for two consecutive weeks, with 59% of drivers exceeding the 90-hour limit. Conversely, in other companies, the predominant issue was insufficient sleep, with high percentages of drivers sleeping less than six hours. The study also found that bus drivers generally reported better sleep quality and quantity than truck drivers but were more likely to exceed legal driving time limits. The significance of this research lies in the development of a simple, reliable, and resource-efficient model for fatigue identification. Unlike technological systems that require physical contact or specific vehicle installations, this model takes only three minutes to apply and relies largely on documented data rather than self-reporting. It allows for the proactive prevention of fatigue-related accidents by enabling companies to rank their safety performance and identify specific operational or behavioral weaknesses. The findings suggest that while passenger transport drivers may have better sleep habits, they face greater risks related to regulatory compliance regarding driving hours, whereas goods transport drivers struggle more with sleep deprivation. This model offers a practical solution for local communities and transport companies to monitor and improve road safety without significant financial investment.
Key finding
The application of the new fatigue identification model revealed that 16.6% of surveyed commercial vehicle drivers were fatigued prior to their shifts, with sleep quality and quantity identified as the most influential factors.
Methodology
survey
Sample size: 265
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- truck driver fatigue
- drowsiness detection algorithms
- drowsiness
- time on task
- sleep deprivation
- shift work driving
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: physiological data
- Methodological Resource: tool software
- Theoretical Contribution: theory or model