A Novel Predictive Modelling Approach Towards a Spatiotemporal Traffic Safety Index
Bibliographic record
Abstract
Traffic safety is now a major concern due to the increase in roadways, traffic, and pedestrians. An index quantifying the traffic safety of a location can help transportation authorities take necessary measures to enhance traffic safety by enforcing Intelligent Transportation System Warrants. All the existing approaches adopt descriptive, inferential, or exploratory data analysis-based statistical techniques to calculate a traffic safety index based on different spatial features of a geographic location. In this paper, we propose a novel predictive modeling approach to calculate a spatiotemporal traffic safety index incorporating both spatial and temporal features based on the National Collision Database Canada from year 1999 – 2019. We conduct Pearson chi-square feature selection and apply feature encoding with categorical embedding. We built a deep learning model based on heterogeneous spatiotemporal features to predict traffic accident severity in terms of human injury and fatality and use it as an indicator for a spatiotemporal traffic safety index. The results show that the proposed methodology can effectively generate a spatiotemporal traffic safety index based on accident severity with an accuracy of 96.26% and surpasses the state-of-the-art approach that has an accuracy of 83%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".