Impact of Local/Short Haul Operations on Driver Fatigue [Report]
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Summary
This report addresses the under-researched safety issues within local/short-haul (L/SH) trucking operations, specifically investigating the role of driver fatigue. While L/SH operations comprise approximately 58% of the trucking industry, prior research has predominantly focused on long-haul drivers. The study was motivated by the need to determine if fatigue is a significant safety factor in L/SH contexts, where drivers typically return home nightly and have varied work routines involving loading, unloading, and customer interaction, unlike the continuous driving of long-haul operators. The research employed a two-phase design. Phase I involved eleven focus groups with 82 L/SH drivers to identify perceived safety and fatigue issues. Phase II was an on-road field study involving 42 drivers from two beverage and snack food companies. Drivers operated instrumented trucks for approximately two weeks each, collecting data via vehicle sensors, video recordings, wrist activity monitors for sleep tracking, and pre- and post-shift questionnaires. The analysis focused on "critical incidents" (near-crashes), categorizing them by fault and examining physiological and behavioral indicators of fatigue, such as PERCLOS (eye closure) and observer-rated drowsiness. The field study recorded 249 critical incidents, with 77 attributed to L/SH driver fault. Fatigue was a contributing factor in 20.8% of these at-fault incidents. Statistical analysis revealed that drivers involved in at-fault incidents exhibited significantly higher drowsiness metrics (PERCLOS and OBSERV) immediately prior to the event compared to other incident types. Crucially, the data indicated that fatigue was primarily brought to the job rather than caused by it; drivers involved in fatigue-related incidents had significantly less sleep quantity and poorer sleep quality than those who did not. Additionally, younger and less experienced drivers were significantly more likely to be involved in at-fault incidents and exhibited higher on-the-job drowsiness. The study also found that a small minority of drivers accounted for the majority of incidents, with two drivers responsible for nearly 26% of all at-fault events. Based on these findings, the authors propose five guidelines to mitigate L/SH safety risks. First, companies should educate drivers on recognizing and managing on-the-job drowsiness and inattention. Second, driver education should emphasize sleep hygiene, as poor off-duty sleep was the primary predictor of on-the-job fatigue. Third, mandatory training programs should be implemented for younger and inexperienced drivers, who were disproportionately involved in incidents. Fourth, companies should implement driver screening processes to identify unsafe drivers prior to hiring, potentially using passenger vehicle records or onboard monitoring systems. Finally, the report suggests public monitoring programs, such as "how's my driving" stickers, to encourage safe performance through external feedback.
Key finding
Fatigue contributed to 20.8 percent of driver-at-fault critical incidents, with affected drivers showing significantly higher drowsiness metrics and poorer sleep quality than their non-fatigued counterparts.
Methodology
field_study
Sample size: 42
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- truck driver fatigue
- time on task
- drowsiness detection algorithms
- drowsiness
- shift work driving
- sleep deprivation
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, crash risk outcomes
- Methodological Resource: dataset resource