Impact of Local/Short-Haul Operations on Driver Fatigue: Field Study [TechBrief]

NHTSA · 2001 · ROSA P / United States. Federal Motor Carrier Safety Administration

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Summary

This study addresses the lack of empirical data regarding driver fatigue in local/short-haul (L/SH) commercial trucking, which constitutes the largest segment of the industry but receives less research attention than long-haul operations. While L/SH drivers typically work daylight shifts and return home daily, they account for 38% of fatal crash involvements. The Federal Motor Carrier Safety Administration (FMCSA) conducted this two-phase research to objectively determine if fatigue is a significant safety issue in L/SH operations, building upon Phase I focus groups that identified fatigue as a concern, though less critical than for long-haul drivers. The Phase II field study involved 42 L/SH drivers from two companies (beverage and snack food haulers) operating four types of trucks. Researchers instrumented vehicles with "black box" data collection systems, including video cameras and vehicle sensors, to capture performance data and critical incidents (near-crashes) over approximately two weeks. Data sources included truck instrumentation, demographic forms, pre- and post-shift questionnaires, and wrist activity monitors to measure sleep quantity and quality. The study analyzed subjective measures (self-reported stress), objective measures (eyelid closure), and physiological measures (sleep patterns) to assess fatigue levels during typical workdays. The findings revealed that L/SH drivers spent 28% of their workday driving and 35% loading/unloading. Researchers identified 249 critical incidents, with 77 attributed to L/SH driver error. Inattention was the most frequent cause (57 incidents), followed by fatigue (28 incidents) and stress due to time pressure (25 incidents). Fatigue was a contributing factor in 21% of driver-at-fault incidents. Objective measures, specifically PERCLOS (eyelid closure) and OBSERV (observer-rated drowsiness), were significantly higher during fatigue-related incidents compared to other events. Wrist monitor data indicated that drivers involved in fatigue-related incidents had less sleep and poorer sleep quality. Notably, fatigue-related incidents occurred primarily early in the workweek, suggesting inadequate rest during weekend breaks. Younger and inexperienced drivers were significantly more likely to be involved in critical incidents and exhibited higher drowsiness levels. Fatigued drivers also demonstrated reduced environmental scanning, fixating on unimportant areas during lane changes and backing maneuvers. The study concludes that off-duty behavior, particularly sleep hygiene, is the primary contributor to on-the-job fatigue in L/SH drivers. The authors recommend that companies encourage drivers to monitor drowsiness, allow short naps, and improve sleep hygiene training. They also suggest enhancing driver screening and training, particularly for younger drivers, and considering special licensing requirements. These findings imply that fatigue management in L/SH operations requires addressing personal lifestyle factors rather than just operational hours, distinguishing it from long-haul fatigue issues.

Key finding

Fatigue contributed to 21 percent of local/short-haul driver-at-fault critical incidents, with affected drivers showing significantly higher eyelid closure and observer-rated drowsiness indicators alongside poorer sleep quality.

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).

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
• 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
• Methodological Resource: dataset resource
• Theoretical Contribution: theory or model