Highlights This paper is dedicated to vehicle-bicycle crash severity modeling. The class-imbalanced issue in vehicle-bicycle crash severity analysis is handled with the imbalanced data resampling process. To address the complexity of crash dataset, the learning-based feature extraction process is adopted in an iterative manner, such that the the most significant contributing factors to the severity of vehicle-bicycle crashes are determined and the trade-off between computation time and model performance is catered for. The vehicle-bicycle crash dataset in this paper contains a large number of discrete variables. The gradient boosting algorithm is applied to handle the large number of categories and does not rely on strict statistical assumptions. The impact of the most significant contributing factors on the severity of vehiclebicycle crashes are explained with the marginal effect analysis. The result can provide some implications for policies and counter-measures for fatal and serious vehicle-bicycle crashes.
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.
Analysis of the severity of vehicle-bicycle crashes with data mining techniques
Journal of Safety Research ; 76 ; 218-227
2020-11-23
10 pages
Article (Journal)
Electronic Resource
English
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