Multimodal Spatiotemporal Deep Fusion for Highway Traffic Accident Prediction in Toronto: A Case Study and Roadmap
Bibliographic record
Abstract
A proactive traffic safety approach provides a forward-looking method for managing traffic and preventing accidents by identifying high-risk conditions before they occur. Previous studies have often focused on historical crash data or demographic factors, relying on limited single-source inputs and neglecting spatial, temporal, and environmental interactions. This study develops a multimodal spatiotemporal deep fusion framework for predicting traffic accidents in Toronto, Canada, by integrating spatial, temporal, environmental, and lighting features within a proactive modeling structure. Three fusion approaches were investigated: (1) environmental feature fusion, (2) extended fusion incorporating lighting and road surface conditions, and (3) a double-stage fusion combining all feature types. The double-stage fusion achieved the best performance, reducing RMSE from 0.50 to 0.41 and outperforming conventional models across multiple error metrics. The framework supports fine-grained hotspot analysis, improves proactive traffic safety management, and provides a transferable roadmap for applying deep fusion in real-world intelligent transportation and urban planning systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".