Monitoring truck driver working and rest hours using safety applications
Bibliographic record
Abstract
Traffic accidents remain a major risk in commercial truck transportation, both in Indonesia and globally. A key contributing factor is driver fatigue, often resulting from excessive workloads and prolonged working hours. This study employs a descriptive case series design to evaluate the implementation of working and rest hour regulations for commercial truck drivers. Secondary data were analyzed from the third quarter of 2024 (July–September), covering three operational regions in West Java, Central Java, and East Java & Bali-Nusa Tenggara. Data were obtained from two driver safety monitoring applications used by a commercial transport operator. Initial analysis involved calculating compliance percentages across all locations in the three regions, followed by a more detailed review of three selected sites per region. Findings reveal that one region exhibited the lowest compliance with regulated working hours and demonstrated inconsistent enforcement of the required 8-hour rest period following 12 hours of work. These results highlight the need for strengthened monitoring and enforcement mechanisms to improve driver safety and reduce accident risks in the commercial transportation sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".