Daily Collision Prediction with SARIMAX and Generalized Linear Models on the Basis of Temporal and Weather Variables
Bibliographic record
Abstract
Short-term collision prediction is a relatively new area of research in the field of traffic safety because of the high randomness of data and the methodological complexity. Motivated by requirements from frontline traffic operations and enforcement services, the authors conducted this study to develop models that predicted daily total collisions. The study started with decomposition analysis of time series data to determine trends, seasonality, and randomness of daily collisions before it proceeded with an investigation of potential collision contributors. Temporal factors (i.e., months, weekdays, and holidays) and weather forecasts (i.e., daily mean temperature, amount of rainfall, and amount of snowfall) were selected as predictive factors. Accordingly, the seasonal autoregressive integrated moving average model with external regressors (SARIMAX) was identified, and a series of SARIMAX models of different orders was estimated and diagnosed. A generalized linear model (GLM) was also developed and compared with the SARIMAX models by validation measures. Finally, a calibration mechanism was recommended to optimize predictions. Model validations provide evidence that both SARIMAX and GLM are adaptable; however, the SARIMAX models are a viable and preferable option because they can provide greater accuracy than GLM in the short-term prediction of collisions. The models developed in this paper are now being applied (a) to support scheduling of traffic operations, maintenance and enforcement, and dispatch of material and personnel resources and (b) to provide situation awareness for all road users and stakeholders.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".