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Open AccessArticle

Identify Risk Pattern of E-Bike Riders in China Based on Machine Learning Framework

Intelligent Transportation System Research Center Southeast University, Nanjing 211189, China
Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1084;
Received: 28 September 2019 / Revised: 31 October 2019 / Accepted: 31 October 2019 / Published: 6 November 2019
(This article belongs to the Special Issue Statistical Inference from High Dimensional Data)
In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e. non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China.
Keywords: e-bike rider; crash risk; machine learning; traffic violation e-bike rider; crash risk; machine learning; traffic violation
MDPI and ACS Style

Wang, C.; Kou, S.; Song, Y. Identify Risk Pattern of E-Bike Riders in China Based on Machine Learning Framework. Entropy 2019, 21, 1084.

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