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

1
School of Highway, Chang’an University, Xi’an 710064, China
2
Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(5), 1327; https://doi.org/10.3390/su11051327
Received: 14 February 2019 / Revised: 21 February 2019 / Accepted: 24 February 2019 / Published: 4 March 2019
(This article belongs to the Section Sustainable Transportation)
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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. View Full-Text
Keywords: bicyclist; hit-and-run; traffic safety; classification and regression tree; data imbalance bicyclist; hit-and-run; traffic safety; classification and regression tree; data imbalance
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Zhou, B.; Li, Z.; Zhang, S.; Zhang, X.; Liu, X.; Ma, Q. Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment. Sustainability 2019, 11, 1327.

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