A Safety Planning Tool for Evaluating Safety Impacts of Road Infrastructure Projects at the Link and Intersection Level
Bibliographic record
Abstract
The current traffic safety literature on methods and factors affecting vehicle-vehicle and vehicle- pedestrian collision frequency is very extensive; however, the integration of collision prediction models (CPMs) at the planning stage to assess the regional safety impacts of roadway infrastructural changes is an area that requires further study. Keeping this in mind, the objectives of this research were two-fold: i) develop a crash-risk prediction tool that combines a macroscopic regional-level traffic assignment model and safety performance functions to evaluate the impact of new road infrastructure and ii) to illustrate its applicability through the evaluation of a major highway project at the intersection and link level. The developed tool automates the collision estimation and mapping for different road network scenarios and road users in the Montreal region. In terms of model specification, different negative binomial (NB) model settings were attempted and selected for the two types of network elements (intersections and links) and the two main types of crashes (vehicle-vehicle and vehicle-pedestrian collisions). After applying the developed tool to a case study, it was found that the model results predict a significant re-distribution of crashes on the network. The cause of the re-distribution is mainly due to the change in the traffic patterns resulting from the opening of the new toll bridge. In addition, the overall impact of this new infrastructure is found to be marginally positive given that the total number of collisions in the overall network slightly decreased. The application also shows the network elements in which safety deteriorates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".