A Flexible Modeling Approach Using Dirichlet Process Mixtures: Application to Multi-Level Railway Grade Crossing Crash Data
Bibliographic record
Abstract
This paper introduces a new approach to addressing two of the most challenging issues in road safety research, namely, how to account for unobserved heterogeneity and how to identify latent subpopulations in data. Compared to the approaches of applying random effects/parameters models and finite mixtures, the proposed approach employs a Bayesian semi-parametric methodology based on Dirichlet process mixtures. Our method has four noteworthy advantages: (i) it allows examining the robustness of distributional assumptions in random effects/parameters models; (ii) it allows identifying latent clusters in data; (iii) it enables identification of outliers (extreme observations) while allowing accommodating them in analyses without compromising the quality of estimates; and (iv) it is capable of estimating the number of latent clusters in data using an elegant mathematical structure. In this paper, we evaluate the proposed method on a railway grade crossing crash dataset with hierarchical (multilevel) structure, at municipality level, from Canada for the years 2008 to 2013. We use cross-validation predictive densities and pseudo Bayes factor for Bayesian model selection. While confirming the need for the multilevel modeling approach, the results pointed out the inadequacy of the parametric assumption. In fact, our proposed method improved model fitting significantly for the municipality-level data. In a fully probabilistic framework, we also identified the expected number of latent clusters with similar unknown/unmeasured features among 81 Canadian municipalities. It is possible thus to further investigate the reasons behind such similarities and dissimilarities, which could have important policy implications in terms of safety management process.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| 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".