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

IF 1.4 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Journal of research in health sciences Pub Date : 2023-06-01 DOI:10.34172/jrhs.2023.116
Fatemeh Jahanjoo, Homayoun Sadeghi-Bazargani, Mohammad Ali Mansournia, Seyyed Teymoor Hosseini, Mohammad Asghari-Jafarabadi
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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, 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.

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 ( χ230 =81.29, P<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).

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.

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随机森林与广义路径分析的混合:52,524个郊区交通事故的因果模型。
背景:确定郊区交通事故的危险因素,可以采取早期和可操作的安全措施,以发现主要的危险因素和调节交通事故的作用。因此,本文重点研究了一个因果建模框架。研究设计:横断面研究。方法:对2015 - 2016年52524起郊区交通事故进行调查。采用混合随机森林广义路径分析技术(HRF-gPath)提取主变量,识别调节因子和调节因子。结果:本研究使用射频模型分析了42个解释变量,发现碰撞类型、不同程度、驾驶员不当行为、速度、执照、先前原因、牌匾描述、车辆机动、车辆类型、照明、乘客存在、安全带使用和土地使用是显著因素。利用g-Path进一步分析表明,碰撞类型、车辆类型、安全带使用和驾驶员不当行为在事故发生中的中介和预测作用。修正后的模型拟合数据较好,具有统计学意义(χ230 =81.29, p)。结论:我们的研究结果发现碰撞类型、车辆类型、安全带使用情况和驾驶员不当行为等几个显著变量发挥了中介和预测作用。这些发现通过理论框架对导致碰撞的复杂因素提供了有价值的见解,并可以为未来减少碰撞发生的努力提供信息。
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来源期刊
Journal of research in health sciences
Journal of research in health sciences PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
2.30
自引率
13.30%
发文量
7
期刊介绍: The Journal of Research in Health Sciences (JRHS) is the official journal of the School of Public Health; Hamadan University of Medical Sciences, which is published quarterly. Since 2017, JRHS is published electronically. JRHS is a peer-reviewed, scientific publication which is produced quarterly and is a multidisciplinary journal in the field of public health, publishing contributions from Epidemiology, Biostatistics, Public Health, Occupational Health, Environmental Health, Health Education, and Preventive and Social Medicine. We do not publish clinical trials, nursing studies, animal studies, qualitative studies, nutritional studies, health insurance, and hospital management. In addition, we do not publish the results of laboratory and chemical studies in the field of ergonomics, occupational health, and environmental health
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