Transportation-Related Human Factors in High-Altitude Regions: Review, Needs, and Novelties
Bibliographic record
Abstract
The low pressure at high altitudes (above 2500 m) causes hypoxia (decreased oxygen) that affects people’s physiological and psychological characteristics. Specifically, hypoxia may affect the neural function of the brain, leading to severe cognitive deficits and a significant decline in memory function and attention. This article addresses the effect of human factors on transportation design and operation at high altitudes (HA), with some details on the Tibet-China region. Specifically, the paper first reviews the basic transportation-related concepts for high altitude, including oxygen and temperature levels, driver perception-reaction time, hazard perception, vehicle speed, and walking speed. Then, the transportation users affected by high altitudes are discussed, including drivers, pedestrians, cyclists, passengers, and others. Next, the impacts of human factors on highway design and operation for HA regions are discussed along with the research needs. Finally, recent innovations to address the challenges of HA transportation are presented, along with case studies comparing some human factors of the plateau and plain areas. This article represents a valuable reference for future research in HA regions to improve transportation design and safety.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".