@article{ref1,
title="Analysis of the severity of vehicle-bicycle crashes with data mining techniques",
journal="Journal of safety research",
year="2021",
author="Zhu, Siying",
volume="76",
number="",
pages="218-227",
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. <br><br>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. <br><br>RESULTS: The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes.<p /> <p>Language: en</p>",
language="en",
issn="0022-4375",
doi="10.1016/j.jsr.2020.11.011",
url="http://dx.doi.org/10.1016/j.jsr.2020.11.011"
}