Shared e-bike riders' psychology contribution to self-reported traffic accidents: a structural equation model approach with mediation analysis

With the rise of the transportation mode of shared electric bikes (shared e-bikes) in China, shared e-bike related accidents have gradually increased. To facilitate the design of safety policies, it is important to understand the factors that influence shared e-bike riders' traffic accidents to facilitate intervention strategies. For this purpose, the structural equation model (SEM) with mediation analysis was applied by incorporating seven latent factors: traffic accidents, traffic violation behaviors, attitude toward safety responsibility, and attitude toward rule violations, risk perception, perceptive-motor skills, and safety skills. A questionnaire survey of a sample of 406 shared e-bike riders in China was conducted to obtain self-reported survey data. The results reveal that traffic violation behaviors and attitude toward safety responsibility had a statistically significant consequence on traffic accidents. Attitude toward rule violations, perceptive-motor skills, and safety skills can predict shared e-bike riders' traffic accidents when the traffic violation behaviors are used as a mediator. Moreover, risk perception could also be used to predict shared e-bike riders' traffic accidents when using attitudes toward safety responsibility or rule violations and traffic violation behaviors as a mediator. This paper lays a foundation for policymakers and traffic managers to develop effective intervention strategies and improve shared e-bike safety.

Automated summary

This study analyzed the influencing factors of traffic accidents among shared e-bike riders in China through a questionnaire survey and a structural equation model. The results showed that traffic violation behaviors and attitude toward safety responsibility had significant effects on traffic accidents. Attitude toward rule violations, perceptive-motor skills, and safety skills could predict traffic accidents when traffic violation behaviors were used as a mediator. Additionally, risk perception could also predict traffic accidents using attitudes toward safety responsibility or rule violations and traffic violation behaviors as mediators. This study provides a foundation for policymakers and traffic managers to develop effective intervention strategies and improve shared e-bike safety.