@article{ref1,
title="Investigation of vehicle-bicycle hit-and-run crashes",
journal="Traffic injury prevention",
year="2020",
author="Zhu, Siying",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVES: Although cycling has been promoted around the world as a sustainable mode of transportation, bicyclists are among the most vulnerable road users, subject to high injury and fatality risk. The vehicle-bicycle hit-and-run crashes degrade the morality and result in delays of medical services provided to victims. This paper aims to determine the significant factors that contribute to drivers' hit-and-run behavior in vehicle-bicycle crashes and their interdependency based on a 6-year crash dataset of Victoria, Australia, with an integrated data mining framework.   METHODS: The framework integrates imbalanced data resampling, near zero variance predictor elimination, learning-based feature extraction with random forest algorithm, and association rule mining.   RESULTS: The crash-related features that play the most important role in classifying hit-and-run crashes are identified as collision type, gender, age group, vehicle passengers involved, severity of accident, speed zone, road classification, divided road, region and peak hour.   CONCLUSIONS: The result of the paper can further provide implications on the policies and counter-measures in order to prevent bicyclists from vehicle-bicycle hit-and-run collisions.<p /> <p>Language: en</p>",
language="en",
issn="1538-9588",
doi="10.1080/15389588.2020.1805444",
url="http://dx.doi.org/10.1080/15389588.2020.1805444"
}