Human factors affecting truck – vulnerable road user safety: a scoping review
Bibliographic record
Abstract
Truck collisions with vulnerable road users (VRUs) are infrequent, but often severe or fatal. While research has investigated factors contributing to safety and collisions, a synthesis of human-related contributors remains missing. This scoping review follows PRISMA guidelines to synthesise studies on the effects of human factors on truck-VRU safety. With a focus on human factors related to pedestrians, cyclists, and truck drivers, five scientific databases were searched and 3,414 records were screened. Twenty-four articles met the inclusion criteria. Most were published after 2015, indicating a limited but growing focus on human factors affecting truck-VRU safety. Half of the studies analysed police collision reports, while others used qualitative data collection methods, like questionnaires and focus groups, or human subject experiments. Compared to passenger-vehicle literature, notably fewer behavioural studies were identified, highlighting a need for further behavioural human factors studies of truck-VRU interactions. Sociodemographic and vision-related factors were most frequently investigated and found to significantly affect collision occurrence and severity. Driving and cycling experience and training, and road user distractions were examined less, albeit being important. This review bridges a literature gap by focusing on human characteristics, states, decisions, and errors affecting truck-VRU safety, offering insights for road-user-centred mitigation strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".