Citation

Zhu S. J. Saf. Res. 2021; 76: 218-227.

Copyright

(Copyright © 2021, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2020.11.011

PMID

unavailable

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.

Language: en

Keywords

Safety; Severity; Gradient boosting algorithm; Integrated data mining framework; Vehicle-bicycle crashes