A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas Publisher Pubmed



Jahanjoo F¹ ; Sadeghibazargani H¹ ; Mansournia MA² ; Hosseini ST³ ; Asgharijafarabadi M^{1, 4, 5, 6} Show Affiliations
Source: Journal of Research in Health Sciences Published:2023

Abstract

Background: Determining 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 Design: A cross-sectional study. Methods: In this study, 52 524 suburban crashes were investigated from 2015 to 2016. The hybrid-randomforest- generalized-path-analysis technique (HRF-gPath) was used to extract the main variables and identify mediators and moderators. Results: This 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 (formula present) and high values for comparative-fit-index and Tucker-Lewis-index exceeding 0.9, as well as a low rootmean square-error-of-approximation of 0.031 (90% confidence interval: 0.030-0.032). Conclusion: The 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. © 2023 The Author(s); Published by Hamadan University of Medical Sciences.