Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment

Bei Zhou, Zongzhi Li, Shengrui Zhang, Xinfen Zhang, Xin Liu and Qiannan Ma
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Bei Zhou: School of Highway, Chang’an University, Xi’an 710064, China
Zongzhi Li: School of Highway, Chang’an University, Xi’an 710064, China
Shengrui Zhang: School of Highway, Chang’an University, Xi’an 710064, China
Xinfen Zhang: School of Highway, Chang’an University, Xi’an 710064, China
Xin Liu: School of Highway, Chang’an University, Xi’an 710064, China
Qiannan Ma: School of Highway, Chang’an University, Xi’an 710064, China

Sustainability, 2019, vol. 11, issue 5, 1-14

Abstract: Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.

Keywords: bicyclist; hit-and-run; traffic safety; classification and regression tree; data imbalance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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