A Novel Approach for Identifying, Diagnosing, and Treating Active Transportation Safety Issues
Bibliographic record
Abstract
There has been an increasing interest in active transportation because of its many health, environmental, and economical benefits. However, active commuters are subjected to an elevated level of severe crash risk, which can be a deterrent to many road users to shift to active transportation. Therefore, there is a need for developing systematic approaches to improve the safety of active commuters. This paper presents a new approach for identifying, diagnosing, and remedying active transportation safety issues. The approach is demonstrated through a case study of the City of Vancouver’s 134 traffic analysis zones. Comprehensive GIS data related to traffic exposure, socio-economics, land use, built environment, street network, and cyclist and pedestrian networks were used in the analysis. A multivariate full Bayesian spatial mixed crash model (CM) was developed incorporating cyclist and pedestrian crashes as well as motorized and non-motorized traffic exposure measures. The CM was used to identify the top 10% active transportation crash-prone zones (CPZs) and safe zones (SZs) using the novel Mahalanobis distance method. CPZs were found clustered in the Downtown. Sixteen trigger variables were statistically investigated for each CPZ and SZ. Lastly, remedies, related to land use, traffic demand, and traffic supply management, were proposed using the trigger variable analysis and literature consultation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".