Analysis of Transit Safety at Signalized Intersections in Toronto, Ontario, Canada
Bibliographic record
Abstract
The objective of the research presented here was to capture the relationship between public transit service configurations and the overall safety performance of signalized intersections in Toronto, Ontario, Canada. Negative binomial regression models were developed for this purpose for three sets of dependent variables: transit-involved collisions at signalized intersections with both regular traffic and transit service operations; total collisions at the same signalized intersections; and total collisions at all signalized intersections, including those without transit service. The models showed that annual average daily traffic, public transit and pedestrian traffic volumes, turn movement treatments, and transit features (such as public transit stop location, mode technology, and availability of transit signal priority technology) all have significant associations with public transit–related collisions at signalized intersections. Intersections with public transit service also tend to experience more collisions than otherwise similar intersections. The research helps to address intersection safety from two perspectives: ( a) it enables public transit providers to consider safety implications in the service planning process, and ( b) it enables transportation departments to assess signalized intersection safety for various configurations of surface transit services by taking into consideration their interaction with the general traffic stream.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".