Investigating the influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling − artificial neural networks approach
Bibliographic record
Abstract
Traffic crashes result from complex interactions between driver, roadway, and environmental factors, which traditional methods often fail to capture. This paper investigates the influence of road, weather, and socioeconomic factors on traffic crashes, using a two-stage hybrid Structural Equation Modelling (SEM)-Artificial Neural Networks (ANN) approach to capture the complex relationships between these factors and crash intensity, a variable that jointly captures the frequency and severity of crashes. A database from Ohio collector road segments served as the case study in this novel hybrid approach, which utilized SEM to analyze the complex and moderating relationships between different factors and crash intensity. SEM revealed significant relationships between crash intensity and factors such as "Horizontal Curve," "Road" (AADT and surface width index), "Segment Length," "Speed Limit," "Vertical Curve," and "Vehicle Possession." Based on the SEM results, "Vehicle Possession" significantly moderated the relationship between "Horizontal Curve" and crash intensity. In the next step, ANN further identified key predictors, including "Segment Length," "Road," the interactions of "Vehicle Possession-Speed Limit," "Vehicle Possession-Vertical Curve," and "Age-Road." The findings highlight the advantage of the complementary application of linear and nonlinear methods in providing invaluable theoretical and methodological insights for crash data analysis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".