Optimizing Intersection Design: Insights From Older Drivers’ Physiological Responses and Gap Acceptance Behavior at Signalized Left Turns
Bibliographic record
Abstract
Aging populations pose significant challenges for transportation safety at complex intersections. This study investigated gap acceptance behavior of older drivers at left-turn signalized permissive intersections using a driving simulator with 40 participants (20 older, mean age$77.95~\pm ~5.79$years; 20 younger, mean age$25.95~\pm ~1.66$years). Participants experienced varying traffic volume levels, queue length, and pedestrian presence. Physiological responses, such as electrodermal activity (EDA) and heart rate variability (HRV), provided insights into drivers’ internal states during decision-making. The results showed that older drivers required longer gap acceptance times compared to younger drivers. Experimental factors like higher traffic volumes, longer queues, and pedestrian presence significantly impacted gap acceptance and led to more conservative decisions. Additionally, higher EDA levels correlated with longer gaps, indicating stress during decision-making. HRV showed a modest yet significant correlation with gap acceptance in older drivers. This suggests that changes in HRV affected their decision-making process, though the influence was weaker than EDA. Significant interactions between traffic volume and queue length and a three-way interaction with pedestrian presence emphasized the complexity of these decisions. Motion sickness susceptibility was also significantly correlated with gap acceptance. These findings contribute to improving road design and driver assistance systems, promoting safer intersections for older drivers.
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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) | 0.000 | 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) | 0.001 | 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".