Investigation of vehicle-bicycle hit-and-run crashes

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.

Keywords:

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