Transit operations, city transportation plans and overall transportation network safety
Bibliographic record
Abstract
Modern transportation planning considers issues such as road congestion, pollution and mobility proactively. However, road safety is usually evaluated in a reactive manner only after the implementation of transportation plans, and when safety problems arise. Although this reactive road safety approach has been very effective, it is associated with significant costs on communities. Therefore, several researchers advocate a more proactive approach to road safety analysis. Several studies developed macro-level Collision Prediction Models (CPMs) that could assess the road safety in a proactive manner, and provide a safety planning decision support tool to community planners and engineers. However, these models have limitations as they do not target the safety evaluation of different goals of a typical city transportation plan. Therefore, the motivation for this research arises 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 could evaluate the safety estimates among alternatives of different transportation plans and policies. The main goal of this thesis is to develop a set of macro-level collision prediction models to investigate the relationships between various transportation and sociodemographic characteristics, and the overall roadway safety. The developed models consider the Poisson variations and the heterogeneity (extra-variation) on the occurrence of collisions and the spatial effects on the distribution of collisions caused by the similarity in environment and geography of the neighbouring sites. It has been shown that the goodness of fit improved with the incorporation of spatial effect. In this study, a set of zonal-level transit reliant and application-based collision prediction models were developed. Data from Metro Vancouver, British Columbia were used to develop models using a generalized linear modelling approach with a negative binomial error structure. Different 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, percentage of commuters walking, percentage of commuters biking, and percentage of commuters using transit. The CPMs related 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".