Models for Safety Analysis of Road Surface Transit
Bibliographic record
Abstract
A study was done to explore the development of zonal- and arterial-level collision prediction models that incorporate characteristics applicable to urban transit planning. A generalized linear modeling approach with a negative binomial regression error structure was employed by using a data set from Toronto, Ontario, Canada. The zonal-level models indicate that vehicle kilometers traveled, bus or streetcar kilometers traveled, arterial road kilometers, bus stop density, percentage of near-sided stops, and average posted speed have significant associations with occurrences of transit-involved collisions. The arterial-level models, which were developed for collisions involving all motor vehicles, suggest that average annual daily traffic, transit frequency, segment length, presence of on-street parking, and percentage of near-sided stops are all associated with increased frequency of these collisions, whereas percentage of far-sided stops and average stop spacing are linked with reduced collision frequency. It is evident that models such as those developed in this study can provide transit agencies with decision-support tools for considering safety implications in the strategic and service-planning processes. These models can also be used as a tool to predict future levels of transit-involved collisions for an existing and a new transportation network or arterial route.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".