Time-Series Forecasting for Peak Hour Traffic Accidents
Bibliographic record
Abstract
Globally traffic accidents cause considerable damage, injuries, and deaths, making their analysis a critical research area. Recent advances have developed various predictions with different method streams yet it is unclear what are the similarities and differences of these streams and how they suit the accident analyses in reality. This study develops time-series accident rate predictions at urban intersections to examine the performance of three streams of the models including statistical model (Negative Binomial Model), machine learning techniques (SARIMA-X) and neural network algorithms (Multi Layer Perceptron, MLP) and further analyzes the suitability of the three streams. Pearson correlation and statistical analysis are first performed to identify the relationships among the spatial-temporal variables (e.g., number of lanes). It is found that the Negative Binomial Model performs superior for the average accuracy of the accident predictions. SARIMA-X performs better for study areas with similar magnitudes of historical traffic accidents over time while MLP is more suitable for accident datasets exhibiting varied magnitudes of accident events. The results provide references and practical insights into the potential of leveraging advanced algorithms and techniques to tackle the dynamics of traffic accidents and improve 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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".