Application of a novel hybrid multigroup statistical approach to investigate the factors affecting crash severity
Bibliographic record
Abstract
Identifying the complex relationships contributing to crash severity is vital for effective road safety strategies but can be challenging. This study explores a hybrid Structural Equation Modeling/Fuzzy-set Qualitative Comparative Analysis (SEM-FsQCA) technique to analyze these relationships, including moderation effects. By integrating SEM and FsQCA to offer a more comprehensive analysis, it overcomes a key challenge of traditional methods-the inability to simultaneously address complex causal relationships and interaction effects. Also investigated was the potential of the Synthesizing Minority Oversampling Technique (SMOTE) for addressing the inherently imbalanced nature of the crash severity and other data used for the analysis. Utilizing a database of Ohio collector roads as a case study, a multigroup analysis was also implemented to analyze factors in lower and higher-income neighbourhoods, which were characterized by imbalanced samples, and assess how combinations of road and environmental variables affect crash severity on roads adjacent to these two neighbourhoods. The SEM results indicated that, regardless of the neighbourhood income level, age, percentage of grade, the proportion of the population having a diploma or higher, horizontal curve, and speed limit all significantly affect crash severity. Those results did indicate that the effects of independent and moderating variables are significantly different for the two neighbourhoods. Using FsQCA, the causal configurations leading to higher crash severity were explored for the two neighbourhood categories. The results of the case study revealed that crash prevention measures could be more effectively developed for crashes based on the income level of neighbourhoods adjacent to the collector roads investigated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".