Evaluating the Safety Estimates of Transit Operations and City Transportation Plans
Bibliographic record
Abstract
Modern transportation planning considers issues such as traffic mobility and pollution proactively. Road safety on the other hand is usually evaluated in a reactive manner, and only when safety problems arise. Therefore, several researchers developed macro-level collision prediction models (CPMs) that could assess road safety in a proactive manner, and provide a safety planning decision support tool to community planners and engineers. However, these models could not target the safety evaluation of different goals of a typical city transportation plan. The motivation for this research arose from the necessity of developing tools that could predict the safety effect of a typical city transportation plan such as changes in the transportation and transit network configurations, and ultimately evaluate the safety level associated with alternatives of different transportation plans and policies. A set of macro-level CPMs was developed to investigate the relationship between various transportation and sociodemographic characteristics, and the overall roadway safety. The developed models considered the Poisson variations and the heterogeneity (extra-variation) on the occurrence of collisions. Data from Metro Vancouver, British Columbia were used to develop models using a generalized linear modelling approach with a negative binomial error structure. Several transit-related variables were found to be statistically significant, namely bus stop density, percentage of transit-km traveled with regard to total vehicle-km traveled, and percentage of commuters walking, biking, and using transit. The developed CPMs were shown to relate total, severe, and property damage only collisions to the implemental aspects related to the goals of long-term transportation plans.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".