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.
Highlights
As a healthy and environmentally friendly transportation mode, cycling has become more and more popular in the United States (US) over the past two decades [1]
According to the League of American Bicyclists, the number of bicycle commuters has been increased by 43% nationwide from 2000 to 2017 in US [7]
Targeting at exploring factors affecting HR and NHR in vehicle-bicycle crashes, this paper has introduced a Classification and Regression Tree (CART) method incorporating data imbalance treatment
Summary
As a healthy and environmentally friendly transportation mode, cycling has become more and more popular in the United States (US) over the past two decades [1]. Previous research has indicated that cycling can provide substantial individual health benefits and alleviate negative impacts of motorized transportation, such as congestion and pollution [2,3,4,5]. According to the League of American Bicyclists, the number of bicycle commuters has been increased by 43% nationwide from 2000 to 2017 in US [7]. The 2017 National Household Travel Survey (NHTS) reported that the bicycle mode share for commuting is only 1.1% [8]. Compared with European cities, the bicycle ridership level is much lower in US [9], indicating a substantial potential for increase
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