Modeling road traffic fatalities in Iran’s six most populous provinces, 2015–2016

Jahanjoo, Fatemeh; Sadeghi‐Bazargani, Homayoun; Asghari‐Jafarabadi, Mohammad

doi:10.1186/s12889-022-14678-5

Cited by 3 publications

(1 citation statement)

References 35 publications

(38 reference statements)

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“…Therefore, to reduce road accidents and its consequence on road traffic, analyzing the characteristics of suburban crashes separately and providing the corresponding statistical model in full detail are of utmost importance. 5 …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Assessing public perception of car automation in Iran: Acceptance and willingness to pay for adaptive cruise control

Sahebi,

Heshami,

Khojastehpour

et al. 2024

IATSS Research

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A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study

Jahanjoo,

Asghari-Jafarabadi,

Sadeghi-Bazargani

2023

BMC Med Res Methodol

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Background Determining risk factors of single-vehicle run-off-road (SV-ROR) crashes, as a significant number of all the single-vehicle crashes and all the fatalities, may provide infrastructure for quicker and more effective safety measures to explore the influencing and moderating variables in SV-ROR. Therefore, this paper emphasizes utilizing a hybrid of regularization method and generalized path analysis for studying SV-ROR crashes to identify variables influencing their happening and severity. Methods This cross-sectional study investigated 724 highway SV-ROR crashes from 2015 to 2016. To drive the key variables influencing SV-ROR crashes Ridge, Least Absolute Shrinkage and Selection Operator (Lasso), and Elastic net regularization methods were implemented. The goodness of fit of utilized methods in a testing sample was assessed using the deviance and deviance ratio. A hybrid of Lasso regression (LR) and generalized path analysis (gPath) was used to detect the cause and mediators of SV-ROR crashes. Results Findings indicated that the final modified model fitted the data accurately with $${\mathcal{X}}_{3}^{2}$$ X 3 2 = 16.09, P < .001, $${\mathcal{X}}^{2}$$ X 2 / degrees of freedom = 5.36 > 5, CFI = .94 > .9, TLI = .71 < .9, RMSEA = 1.00 > .08 (90% CI = (.06 to .15)). Also, the presence of passenger (odds ratio (OR) = 2.31, 95% CI = (1.73 to 3.06)), collision type (OR = 1.21, 95% CI = (1.07 to 1.37)), driver misconduct (OR = 1.54, 95% CI = (1.32 to 1.79)) and vehicle age (OR = 2.08, 95% CI = (1.77 to 2.46)) were significant cause of fatality outcome. The proposed causal model identified collision type and driver misconduct as mediators. Conclusions The proposed HLR-gPath model can be considered a useful theoretical structure to describe how the presence of passenger, collision type, driver misconduct, and vehicle age can both predict and mediate fatality among SV-ROR crashes. While notable progress has been made in implementing road safety measures, it is essential to emphasize that operative preventative measures still remain the most effective approach for reducing the burden of crashes, considering the critical components identified in this study.

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Modeling road traffic fatalities in Iran’s six most populous provinces, 2015–2016

Cited by 3 publications

References 35 publications

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

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

Assessing public perception of car automation in Iran: Acceptance and willingness to pay for adaptive cruise control

A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study

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