Model-Based Versus Data-Driven Approach for Road Safety Analysis: Do More Data Help?
Bibliographic record
Abstract
Crash data for road safety analysis and modeling are growing steadily in size and completeness because of the latest advancement in information technologies. This increased availability of large data sets has generated resurgent interest in applying a data-driven nonparametric approach as an alternative to the traditional parametric models for crash risk prediction. This paper investigates the question of how the relative performance of these two alternative approaches changes as crash data grow. Two popular techniques from the two approaches are compared: negative binomial models for the parametric approach and kernel regression for the nonparametric counterpart. Two large crash data sets are used to investigate the performance of these two methods as a function of the amount of training data. A rigorous bootstrapping validation process shows that the two approaches have strikingly different patterns, especially in sensitivity to data size. The kernel regression method outperforms the model-based approach—that is, negative binomial—for predictive performance, and that performance advantage increases noticeably as data available for calibration grow. With the arrival of the big data era and the added benefits of enabling automated road safety analysis and improved responsiveness to current safety issues, nonparametric techniques (especially those of modern machine approaches) can be counted as an important tool in road safety studies.
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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 | 0.037 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".