Investigating relationships among perceptions of yielding, safety, and comfort for pedestrians in unsignalized crosswalks
Bibliographic record
Abstract
Interactions with other road users influence the perceived safety and comfort of pedestrians. Yet the relationships among perceptions of yielding, safety, and comfort are poorly understood. To enhance understanding of these key concepts, the objectives of this study are to determine how perception of pedestrian safety at unsignalized crosswalks differs from perception of comfort, and the relationship of each with perception of yielding. A generalized structural equations model is developed using data from an online survey in which 366 participants (i.e., “perceivers”) rated yielding, safety, and comfort for sample videos of pedestrian interactions with motor vehicles and bicycles. Results show that an individual’s perception of yielding plays a crucial role in mediating the effects of interaction attributes (e.g., vehicle speed, proximity) and perceiver attributes (e.g., travel habits) on their perceptions of pedestrian safety and comfort. For example, people who bicycle more frequently perceive pedestrians as more comfortable than people who walk more frequently, rooted in misalignment on what constitutes adequate yielding. Strategies to address pedestrian comfort can focus on a set of key yielding behaviors by drivers and cyclists – particularly allowing the pedestrian to cross first. Motor vehicle drivers must exhibit stronger yielding behavior (e.g., allow a larger time gap) than bicycles to achieve the same level of perceived pedestrian safety and comfort. Although perceptions of safety and comfort are strongly related and similarly impacted by yielding, researchers should be cautious about using the concepts interchangeably because they are differently impacted by attributes of the interaction and perceiver.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".