regularizationalgorithm

Determining suburban area crashes' risk factors may allow for early and operative safety measures to find the main risk factors and moderating effects of the crashes. Therefore, this paper focuses on a causal modeling framework. Study design: A cross-sectional study.

In this study, 52,524 suburban crashes were investigated from 2015 to 2016. Hybrid-random-forest-generalized-path-analysis-technique (HRF-gPath) was used to extract the main variables and identify mediators and moderators.

This study analyzed 42 explanatory variables using a RF model and 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 (=81.29, P<0.001) and high values for comparative-fit-index (CFI) and Tucker-Lewis-index (TLI) exceeding 0.9, as well as a low root-mean-square-error-of-approximation (RMSEA) of 0.031 (90% confidence-interval (CI): 0.030 to 0.032).

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.