Microscopic behavioural analysis of cyclist and pedestrian interactions in shared spaces
Bibliographic record
Abstract
This study investigates the microscopic interaction behaviour between cyclists and pedestrians in shared space environments. Video data was collected at the Robson Square shared space in downtown Vancouver, British Columbia. Trajectories of cyclists and pedestrians involved in 208 interactions (416 trajectories) were extracted using computer vision algorithms. The extracted trajectories were used to define different indicators for the analysis. The indicators included the speed and acceleration profiles and the longitudinal and lateral distances between road users during different phases of the interactions. The study also investigated the collision avoidance mechanisms employed by road users to avoid collisions with other shared space users. The collision avoidance mechanisms included changing the walking–cycling speed and changing the movement direction. The results showed that the collision avoidance mechanisms depend on the shared space density and the space available for road users. The study identified a set of parameters that can be used to calibrate microscopic cyclist–pedestrian modeling platforms to represent the behaviour of pedestrians and cyclists in shared space environments.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".