Leveraging Clustering Methods for Enhancing Traffic Safety in the Era of Connected and Autonomous Vehicles in Calgary*
Bibliographic record
Abstract
This research investigates at how clustering techniques can be used to improve traffic safety in Calgary, with a focus on potential applications for connected and autonomous vehicles (CAVs). We identified and analyzed traffic collision hotspots using a two-step cluster analysis combined with association rule mining, using a collision dataset that spanned over eight years. Our approach highlights the flexibility in managing high-dimensional information by applying the DBSCAN and COOLCAT algorithms to identify spatial clusters (namely as collision hotspots in Calgary) and categorize collisions inside these clusters based on categorical data, respectively. Our research reveals important trends in traffic collisions and the elements that lead to them; these trends were then investigated further to create realistic, scenario-based models for CAVs. We combined these patterns into practical insights by utilizing association rule mining, which encourages preventive measures suited to certain high-risk situations. This strategy not only improves traffic management systems' predictive powers but also helps the public embrace and trust future CAV technologies. Our research provides a roadmap for municipalities to incorporate cutting-edge data analysis methods into their traffic safety plans. In the age of CAVs, our study contributes to the creation of safer urban mobility solutions by giving data-driven insights.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".