A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas.
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 stu...
| Publicado en: | Journal of Research in Health Sciences Vol. 23; no. 2; pp. 1 - 11 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Hamadan University of Medical Sciences, School of Public Health
Spring2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169841509&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169841509 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22287795 903Q jtl: Journal of Research in Health Sciences issn: 22287795 maglogo: N pubinfo: dt: Spring2023 vid: 23 iid: 2 pid: 54266 pub: Hamadan University of Medical Sciences, School of Public Health artinfo: ui: 169841509 169841509 169841509 10.34172/jrhs.2023.116 169841509 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas. aug: au: Jahanjoo, Fatemeh Sadeghi-Bazargani, Homayoun Mansournia, Mohammad Ali Hosseini, Seyyed Teymoor Asghari-Jafarabadi, Mohammad affil: Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran sug: subj: Accidents, Traffic Analysis Suburban Areas Accidents, Traffic Risk Factors Risk Assessment Human Cross Sectional Studies Causal Modeling Random Forest Methods Path Analysis Methods Independent Variable Chi Square Test Descriptive Statistics Confidence Intervals Conceptual Framework Accidents, Traffic Prevention and Control Scales ab: 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-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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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