Risk Riding Behaviors of Urban E-Bikes: A Literature Review

Changxi Ma, Dong Yang, Jibiao Zhou, Zhongxiang Feng and Quan Yuan
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Changxi Ma: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Dong Yang: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Jibiao Zhou: School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China
Zhongxiang Feng: School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei 230009, China
Quan Yuan: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China

IJERPH, 2019, vol. 16, issue 13, 1-18

Abstract: In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users’ traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.

Keywords: traffic engineering; e-bikes; risky riding behavior; traffic accidents; interventions (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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