Dataset for the article "Assessing the Safety of Cyclist–Pedestrian Interactions in Seasonal Pedestrian Streets Using Computer Vision Techniques"
Bibliographic record
Abstract
Pedestrian streets, also known as streets closed to motorized traffic, serve to promote active modes of transportation. This concept offers the potential to enhance safety for the most vulnerable road users while concurrently reducing air pollution. The present study aims to evaluate the safety of interactions between pedestrians and cyclists, focusing on three pedestrian streets within the city of Montreal, Quebec. Data collection is performed using cameras during summer days in 2021. Following camera calibration, a total of 80 hours of data is analyzed. Each road user detected and tracked is categorized as either "Pedestrian" or "Cyclist". The analysis involves the computation of indicators for individual cyclists (speed and acceleration) and for their interactions with pedestrians (distance and time to collision (TTC)). Two multivariate regression models were estimated to analyze the relationship between TTC or the cyclist speed as the dependent variables and several other factors. The findings from the safety analysis reveal a discernible variation in safety indicator values among distinct sites, even those situated on the same thoroughfare, independent of regulatory measures. The statistical analysis indicates that elevated values of TTC correspond to high acceleration and increased distances between pedestrians and cyclists. Moreover, high TTC values are associated negatively with the density of pedestrians within the camera's field of view. In contrast, concerning speed, high values are linked to low TTC and distances, along with elevated acceleration values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.042 |
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".