Citation

Zhu S. Traffic Injury Prev. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Informa - Taylor and Francis Group)

DOI

10.1080/15389588.2020.1805444

PMID

32835525

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.

Language: en

Keywords

association rule mining; hit-and-run; integrated data mining framework; learning-based feature extraction; Vehicle-bicycle crashes