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Impact of Wind Turbine Distraction on Crash Severity: Assessment and Prediction Study

Fadi Alhomaidat, Mu'ath Al-Tarawneh, Mousa Abushattal, Renad Al-Alaya

Abstract


Wind turbines are increasingly installed near highways, yet their potential role as external distractions impacting traffic crashes remains underexplored. This study investigates the effect of wind turbines on crash severity and frequency along Jordan’s King’s Highway, analyzing data through a mixed-effect logit model and machine learning techniques. Key factors, including driver demographics, road geometry, and environmental conditions, were incorporated to provide a comprehensive analysis. The findings indicate a 117.4% increase in severe injury crashes (KAB) and a 25.7% rise in property damage only near wind turbines. Using MK models such as Bagged Tree classifiers and SMOT-balanced datasets, the study achieved a high prediction accuracy of 89.6% for crash severity. Shapley value analysis identified crash type and wind turbine proximity as critical predictors, while other influential factors included younger drivers, poorly separated roads, and higher speed limits. By integrating statistical and ML approaches, this research provides actionable insights into the relationship between wind turbines and road safety. The results underscore the need for regulatory policies to optimize wind turbine placement and reduce their potential as driver distractions. This study also demonstrated the potential of ML techniques to enhance traffic safety analysis, paving the way for future research to address multi-class crash severity predictions and other external roadside distractions.

 

Doi: 10.28991/CEJ-2025-011-04-018

Full Text: PDF


Keywords


Wind Turbines; Driver Distractions; Mixed-Effects Logit Model; Machine Learning in Traffic Analysis; Bagged Tree Classifier; Crash Severity.

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Full Text: PDF

DOI: 10.28991/CEJ-2025-011-04-018

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Copyright (c) 2025 Fadi Alhomaidat, Mu'ath Al-Tarawneh, Mousa Abushattal, Renad Alya

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