Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
J Res Health Sci. 2023 Jun;23(2):e00581. doi: 10.34172/jrhs.2023.116.
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.
A cross-sectional study.
In 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.
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 ( χ =81.29, <0.001) and high values for comparative-fit-index and Tucker-Lewis-index exceeding 0.9, as well as a low root-mean-square-error-of-approximation of 0.031 (90% confidence interval: 0.030-0.032).
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.
确定郊区事故的危险因素可以早期采取有效安全措施，发现事故的主要危险因素和调节因素。因此，本文专注于因果建模框架。
横断面研究。
本研究调查了 2015 年至 2016 年的 52524 起郊区事故。采用混合随机森林广义路径分析技术（HRF-gPath）提取主要变量，并识别中介和调节因素。
本研究使用 RF 模型分析了 42 个解释变量，发现碰撞类型、显著特征、驾驶员不当行为、速度、驾照、先前原因、斑块描述、车辆操纵、车辆类型、照明、乘客存在、安全带使用和土地利用是显著因素。进一步使用 g-Path 的分析表明，碰撞类型、车辆类型、安全带使用和驾驶员不当行为具有中介和预测作用。修正后的模型拟合数据良好，统计显著（ χ =81.29，<0.001），比较拟合指数和塔克-刘易斯指数超过 0.9，以及低均方根误差逼近 0.031（90%置信区间：0.030-0.032）。
本研究确定了几个显著变量，包括碰撞类型、车辆类型、安全带使用和驾驶员不当行为，它们具有中介和预测作用。这些发现通过理论框架提供了对导致碰撞的复杂因素的宝贵见解，并可以为未来减少碰撞发生的努力提供信息。
