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• W3115408060 abstract "Introduction: Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist. Method: This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013–2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s. Results: The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes." @default.
• W3115408060 created "2021-01-05" @default.
• W3115408060 creator A5066756187 @default.
• W3115408060 date "2021-02-01" @default.
• W3115408060 modified "2023-10-16" @default.
• W3115408060 title "Analysis of the severity of vehicle-bicycle crashes with data mining techniques" @default.
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• W3115408060 doi "https://doi.org/10.1016/j.jsr.2020.11.011" @default.
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