Speed control, visual adaptation, and mental workload in urban short underpass tunnels: A naturalistic driving study
Bibliographic record
Abstract
OBJECTIVE: Urban short underpass tunnels, characterized by steep longitudinal slopes, limited lengths, and abrupt light transitions, pose significant driving risks. This study aims to comprehensively investigate drivers' speed control behavior, visual adaptation processes, and mental workload mechanisms within such tunnels under real traffic conditions. METHODS: A real-vehicle experiment was conducted involving 35 drivers. Data on speed, acceleration, pupil area, and heart rate were collected. The experimental road segment was divided into five sections: entry, downhill, tunnel, uphill, and departure, to analyze driving behavior characteristics and psychophysiological responses systematically. RESULTS: Speed increased significantly in the downhill section and decreased notably in the uphill and departure sections. The tunnel section exhibited a high incidence of speeding, while the uphill section showed the most significant speed variability, both identified as high-risk zones for rear-end collisions. Regarding visual adaptation, drivers entered a dark adaptation ∼70 m before reaching the tunnel entrance, marked by a gradual increase in pupil area. Around 50 m before the tunnel exit, drivers transitioned to light adaptation, and the pupil area decreased rapidly. Mental workload analysis revealed that drivers' heart rates increased during the downhill section, peaked shortly after entering the tunnel, and gradually declined as light adaptation began. Further analysis showed that acceleration was negatively correlated with heart rate growth rate (HRG), while pupil area change rate (PAC) and vehicle speed positively correlated with HRG. CONCLUSION: This study reveals that speed behavior, visual adaptation, and mental workload in urban short underpass tunnels vary significantly across road sections, with the tunnel and uphill sections posing the highest risk for rear-end collisions, highlighting the need for targeted tunnel design and traffic safety measures.
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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".