How does built environment influence pedestrian activity and pedestrian collisions at intersections?
Bibliographic record
Abstract
"This paper studies the influence of built environment in the vicinity of an intersection on pedestrian activity and collision frequency. In doing so, a two-equation model to predict pedestrian activity and collision occurrence is formulated and validated using data from 509 signalized intersections in the City of Montreal. The applicability of this modeling framework is illustrated through an elasticity analysis and a model validation study. Among other results, it was found that the built environment (BE) in the proximity of an intersection has a powerful association with pedestrian activity but a small direct effect on collision frequency. That is, the impact of BE is mainly mediated through pedestrian activity and traffic volume. In accordance with previous studies, pedestrian activity and traffic volume are the main determinants of pedestrian collision frequency at signalized intersections. Our analysis illustrates how urban policies aiming to increase population density, transit offer and road network connectivity may have important health and safety benefits by encouraging pedestrian activity. In addition, our results show that a reduction of 30% in the traffic volume in each of the studied intersections would greatly reduce the average risk of pedestrian collision (-50%) and the total number of injured pedestrians (-35%) in the area under analysis. Arterials and urban highways seem to have a double negative effect on pedestrian safety: major roads are negatively related with pedestrian activity and positively associated with traffic volume. Those results support the idea of retrofitting major urban roads into more complete streets. "@eng
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".