Perceived safety and experienced incidents between pedestrians and cyclists in a high-volume non-motorized shared space
Bibliographic record
Abstract
Investigation of pedestrian-cyclist interactions is important for understanding both objective risks and traveler comfort. There is a lack of clear understanding of conflicts between pedestrians and cyclists, as they result in few reported incidents or injuries. More research is needed on the frequency and causes of pedestrian-cyclist incidents, and how those incidents affect the comfort and perceived safety of travelers by each mode. The objectives of this research were 1) to investigate the relationship between expressed safety concerns and experienced incidents for travelers in a high-volume non-motorized shared space, and 2) to examine primary factors in pedestrian-cyclist incidents. Data were acquired from an intercept survey of 337 travelers conducted on a large university campus. Results reveal a high frequency of incidents with physical contact between people walking and cycling, consistently described by each, which led to few injuries but verified the expressed concerns of survey participants and anecdotal reporting to the campus transportation agency. Prior experience of an incident was a significant factor influencing perceptions of safety. Cyclists were at least as concerned about intermodal conflicts and safety as pedestrians and preferred to avoid pedestrian-dominated areas, but that preference was weighed against travel time, ease of wayfinding, and avoidance of motor vehicles. Both pedestrians and cyclists identified crowding and pedestrian inattention as major contributing factors to incidents, but they disagreed on whether cyclist speed was a factor. Potential strategies to reduce intermodal conflicts in shared spaces include reducing crowding, separating modes, and reducing cycling speeds.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".