ArticleJournal of research in health sciences2023
A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas.
Article in Journal of research in health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 0 citations in OpenAlex.
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Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetermining suburban area crashes' risk factors may allow for early and operative safety measures to find the main risk factors and moderating effects of crashes. Therefore, this paper has focused on a causal modeling framework. STUDY
designA cross-sectional study.
methodsIn this study, 52524 suburban crashes were investigated from 2015 to 2016. The hybrid-random-forest-generalized-path-analysis technique (HRF-gPath) was used to extract the main variables and identify mediators and moderators.
resultsThis study analyzed 42 explanatory variables using a RF model, and it was found that collision type, distinct, driver misconduct, speed, license, prior cause, plaque description, vehicle maneuver, vehicle type, lighting, passenger presence, seatbelt use, and land use were significant factors. Further analysis using g-Path demonstrated the mediating and predicting roles of collision type, vehicle type, seatbelt use, and driver misconduct. The modified model fitted the data well, with statistical significance ( χ
conclusionThe results of our study identified several significant variables, including collision type, vehicle type, seatbelt use, and driver misconduct, which played mediating and predicting roles. These findings provide valuable insights into the complex factors that contribute to collisions via a theoretical framework and can inform efforts to reduce their occurrence in the future.
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