How COVID-19 impacted the temporal and spatial distribution of collision hotspots
Bibliographic record
Abstract
This research examines the spatial and temporal shift in collision hotspots caused by the COVID-19 pandemic, considering different collision severities. The Getis-Ord statistic was utilized to create spatial models and generate map outputs for 2019 and 2020. Two distinct approaches were employed: using a census tract shapefile (provided) and creating fishnet polygons measuring 500 m by 500 m. Results showed fewer hotspots outside Edmonton's central core, while fatal collisions were concentrated close to the core. This intriguing finding suggests that COVID-19 restrictions led to more aggressive driving behaviour near the centre, contributing to a rise in fatal collision numbers. The study found a significant reduction in traffic collisions in April 2020, with a 58% decrease compared to the previous year. The research highlights the pandemic's impact on road safety, emphasizing the importance of reducing traffic volume and advocating for traffic restrictions and control strategies, multi-modal planning, and efficient pricing strategies within Vision Zero for improved road safety.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".