Exploring Pedestrian Road Safety in Public Transit Locations
Bibliographic record
Abstract
This thesis studies the magnitude of pedestrian road collisions in public transit locations and addresses how the road and built-environment elements affect pedestrian safety at public transit access points (PTAPs). Collision count models and hotspot identification methods are utilized to address the research questions. Chapter 1 and Chapter 2 provide an introduction and literature review over pedestrian road safety in general, and specifically in public transit locations. Chapter 3 explains the methodologies that will be utilized in this research study. Chapter 4 establishes a relationship between pedestrian-vehicle collision counts and public transit services. Pedestrian collisions occur more frequently at intersections with the presence of a PTAP and with a higher bus traffic volume, a higher number of bus routes, and a higher public transit accessibility index. Hence, Chapter 5, explores how road geometry and built environment elements affect pedestrian-vehicle collision counts at PTAPs. The analysis shows that strategies such as road narrowing, sidewalk width increase, median refuges, presence of signal’s walk interval, and vehicle stop signs could improve pedestrian safety at PTAPs. Moreover, pedestrians are at more risk in PTAP where there are roads with higher road grades and more two-way streets than one-way streets. Chapter 5 continues with Empirical Bayes collision hotspot identification and examines a couple of collision hotspots in PTAPs in the case study of Montreal City. The study findings point out the need to improve pedestrian road safety at PTAP locations and offer engineering countermeasures for addressing this problem.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".