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Abstract

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.