Macrolevel Collision Prediction Models to Evaluate Road Safety Effects of Mobility Management Strategies: New Empirical Tools to Promote Sustainable Development
Bibliographic record
Abstract
Mobility management (also called Transportation Demand Management, or TDM) consists of various strategies that change travel behavior to increase transportation system efficiency. Mobility management policies and programs are generally promoted as ways to reduce traffic congestion, parking problems and pollution emissions; road safety is seldom a major objective. However, research described in this paper indicates that mobility management strategies also provide significant safety benefits. This paper describes how community-based, macro-level collision prediction models (CPMs) can be used to calculate the road safety effects of specific mobility management strategies (MMS). It summarizes the results of road safety evaluations of three mobility management strategies using recently developed macro-level CPMs, and using data from 479 urban neighborhoods in the Greater Vancouver Regional District (GVRD), in British Columbia (BC), Canada. The results suggest that a smart growth strategy of more compact, multi-modal land use development patterns can reduce per capita neighborhood collision frequency by 20% (total) and 29% (severe); that a congestion pricing strategy has the potential to reduce neighborhood collision frequency by 19% (total) and 21% (severe); and improving transportation options (better walking and cycling conditions, and improved ridesharing and public transit services) could reduce collision frequency by 14% (total) and 15% (severe). These model predictions are consistent with actual observed mobility management collision reductions. This study indicates that mobility management strategies can significantly increase traffic safety in addition to providing other economic and environmental benefits.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".