A skewed logistic model of two-unit bicycle-vehicle hit-and-run crashes

Abstract

Objective

Hit-and-run behavior in crashes is a severe offense worldwide because the identification and emergency rescue of any injured road user is delayed. A motorist’s run from the crash scene is especially serious for a cyclist who would be more prone to be physically injured in a bicycle-vehicle (BV) crash. The objective of this paper is to explore potential risk factors that contribute to the hit-and-run (HR) behavior of a driver after a two-unit BV collision.

Methods

The data used in this study are extracted from traffic crash records in the city of Durham, North Carolina in 2007–2014. This study uses the skewed logistic (Scobit) model to account for the skewness of the dependent variable (i.e., HR) in the dataset.

Results

The Likelihood ratio test, AIC and BIC results show that the Scobit model is preferred to the standard binary logistic model for modeling a driver’s decision to run from a two-unit BV crash scene. Estimation results indicate that, the driver’s tendency to run from a crash scene without reporting it in Durham increases if the bicyclist is a teenager or an adult, a drunk-driving or a speeding driver is involved, when the crash happens at night (19:00–6:59), on a local street, or when the automobile overtakes the bicycle. HR behavior will decrease if the cyclist is drunk, an SUV is involved, or the bicyclist fails to yield.

Conclusions

The findings of this study are important and useful when developing countermeasures to prevent BV-HR crashes and to improve cycling safety.

Keywords:

• Hit-and-run
• road safety
• bicycle-vehicle crash
• logistic model
• skewed logistic (Scobit) model

Acknowledgment

The authors are indebted to two anonymous reviewers and the associate editor for their constructive comments on improving the presentation of this paper.

Additional information

Funding

This study was partially funded by the Fund of Cultivation Project for Young University Teachers from Shanghai Municipal Education Commission (grant number: ZZHS18001) and the Cultivation Fund for Top Journal Papers of Shanghai Maritime University.