Physiological markers of traffic-related stress during active travel
Bibliographic record
Abstract
Understanding perceptions of safety and comfort (PSC) while walking or cycling is essential to accommodating and encouraging active travel, but current measures of PSC, primarily surveys, suffer from validity and reliability issues. Physiological markers of stress like electrodermal activity and heart rate variability have been proposed as alternative, objective measures of PSC. This paper presents a literature summary and conceptual framework examining the use of physiological stress markers during walking and cycling. The existing studies of active traveller stress markers report inconsistent findings and account for limited controls. We propose a comprehensive conceptual framework to describe the array of dynamic stimuli experienced during active travel, with complex appraisals and multidimensional stress responses that feedback to travel behaviour and stimuli exposure, and culminate in a set of physiological outcomes triggered by activation of the autonomic nervous system – all moderated by numerous personal and trip-related factors. The key challenge of inferring traffic-related fear or discomfort from physiological markers measured on-road is potential confounding effects of: (1) non-traffic factors that induce or modify stress responses, (2) traffic factors that induce stress responses not associated with safety or comfort, and (3) personal and environmental factors that directly influence physiological measurements outside of a stress response. No physiological stress marker has yet been shown to be reliable for on-road active travellers, particularly not for inter-subject comparisons. Physiological markers have the potential to provide high-resolution, objective information about pedestrian and cyclist PSC, but further research, particularly controlled experiments, and more precise study framing are needed to ensure validity and address moderating and confounding factors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".