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Journal of Advanced Transportation
Volume 2017, Article ID 2142659, 12 pages
Research Article

Evaluation of Factors Affecting E-Bike Involved Crash and E-Bike License Plate Use in China Using a Bivariate Probit Model

1Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Sipailou No. 2, Nanjing 210096, China
2Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC, Canada V6T 1Z4
3School of Civil and Transportation Engineering, Ningbo University of Technology, Fenghua Rd. No. 201, Ningbo 315211, China
4Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Huaiyin Institute of Technology, Meicheng Rd. No. 1, Huaiyin 223003, China

Correspondence should be addressed to Yanyong Guo; ac.cbu@oug.gnoynay and Jibiao Zhou; moc.621@666oaibuohz

Received 19 June 2017; Revised 21 October 2017; Accepted 24 October 2017; Published 21 November 2017

Academic Editor: Richard S. Tay

Copyright © 2017 Yanyong Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The primary objective of this study is to evaluate factors affecting e-bike involved crash and license plate use in China. E-bike crashes data were collected from police database and completed through a telephone interview. Noncrash samples were collected by a questionnaire survey. A bivariate probit (BP) model was developed to simultaneously examine the significant factors associated with e-bike involved crash and e-bike license plate and to account for the correlations between them. Marginal effects for contributory factors were calculated to quantify their impacts on the outcomes. The results show that several contributory factors, including gender, age, education level, driver license, car in household, experiences in using e-bike, law compliance, and aggressive driving behaviors, are found to have significant impacts on both e-bike involved crash and license plate use. Moreover, type of e-bike, frequency of using e-bike, impulse behavior, degree of riding experience, and risk perception scale are found to be associated with e-bike involved crash. It is also found that e-bike involved crash and e-bike license plate use are strongly correlated and are negative in direction. The result enhanced our comprehension of the factors related to e-bike involved crash and e-bike license plate use.