Understanding electric bike riders’ intention to violate traffic rules and accident proneness in China

As electric bicycles (e-bikes) have emerged as an important transportation mode in China in the past decade, e-bike-related accidents have increased drastically. Research suggests that the main cause of most of these accidents is traffic rule violations by e-bike riders and that some e-bike riders have a higher propensity to experience accidents (i.e., higher accident proneness) than otherwise similar individuals. To facilitate the design of safety policies, it is important to understand the factors that influence both e-bike riders’ intention to violate traffic rules and accident proneness. For this purpose, an extension of the theory of planned behavior framework (E-TPB) was developed by incorporating seven new latent psychological factors (descriptive norm, moral norm, perceived risk, self-identity, legal norm, conformity tendency, and past behavior) into the original TPB framework (O-TPB). Using self-reported survey data from over 2000 e-bike riders collected in Shanghai, China, structural equation models for the E-TPB and the O-TPB were estimated. The model estimation results show that the E-TPB provides a more intuitive explanation of e-bike riders’ intention to violate traffic rules and accident proneness and has superior predictive power compared to the O-TPB. The model estimation results also show that descriptive norm, conformity tendency, and past behavior are important factors that affect both e-bike riders’ intention to violate traffic rules and accident proneness. These findings can be used by policymakers to design safety policies such as reward programs for safe riding behavior, e-bike rider education initiatives, and behavior modification interventions to improve road safety.

• Record URL:
  https://doi.org/10.1016/j.tbs.2020.10.010
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2214367X20302325
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/2214367X
• Supplemental Notes:
  • © 2020 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Tang, Tianpei
  • Guo, Yuntao
  • Zhou, Xizhao
  • Labi, Samuel
  • Zhu, Senlai
• Publication Date: 2021-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 25-38
• Serial:
  • Travel Behaviour and Society
  • Volume: 23
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-367X
  • Serial URL: http://www.sciencedirect.com/science/journal/2214367X

Subject/Index Terms

• TRT Terms: Bicycle crashes; Crash risk forecasting; Cyclists; Electric vehicles; Safety; Traffic violations
• Geographic Terms: Shanghai (China)
• Subject Areas: Law; Operations and Traffic Management; Pedestrians and Bicyclists;

Filing Info

• Accession Number: 01760495
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 3 2020 3:16PM