Bibliographic record
Abstract
This paper describes the We're All Pedestrians Program. It documents the effectiveness of three pedestrian safety initiatives at signalized intersections. The study evaluates the benefits and costs and applicability of the safety initiatives, including Intelligent Transportation System (ITS) applications, across the 1,800 signalized intersections in the City of Toronto. The We're All Pedestrians Program is proactive program to enhance pedestrian safety. The program has been initiated by the City of Toronto in recognition of the importance of pedestrian travel and the fact that pedestrians represent the most vulnerable users of the transportation system. The purpose of the study is to measure the effectiveness of three measures to reduce the frequency of vehicle-pedestrian conflicts and collisions through field-testing and site evaluation. The study assessed the effectiveness of implementing Broad Pavement Markings, Leading Pedestrian Intervals, and Passive Pedestrian Detection Devices, through before and after studies of driver and pedestrian behavior. It quantifies changes in the frequency of yielding behavior of drivers and vehicle-pedestrian conflicts. The We're All Pedestrians Program is intended to provide statistically significant measures of effectiveness. The analysis includes research into the relationship between conflict frequency and collision frequency and a human factors assessment of driver and pedestrian behavior. The products of the process include a benefit-cost assessment of the measures and discussion of the potential for developing collision modification factors.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".