Predicting e-bike users’ intention to run the red light: An application and extension of the theory of planned behavior

• Published in: Transportation research. Part F, Traffic psychology and behaviour Vol. 58; pp. 282 - 291
• Main Authors: Yang, Hongtai, Liu, Xiaohan, Su, Fan, Cherry, Christopher, Liu, Yugang, Li, Yanlai
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2018; Elsevier Science Ltd
• Subjects: Behavior; Bicycles; Construction planning; Demographic variables; Demographics; Drivers licenses; Electric two-wheeler; Motivation; Non-motorized vehicle; Railroad accidents & safety; Regression analysis; Riders; Riding behavior; Social psychological model; Studies; Traffic violation; China; Electric two-wheeler; Non-motorized vehicle; Traffic violation; Riding behavior; Social psychological model
• ISSN: 1369-8478; 1873-5517
• DOI: 10.1016/j.trf.2018.05.027

•We examine the psychological motivation of e-bike users’ red light running behaviors using theory of planned behavior.•Attitude and perceived behavioral control had significant positive impacts on intention.•Moral norm and self-identity had significant negative influence on intention.•There are significant differences between the means of psychological variables of different population groups.•Effective interventions such as safety education programs should be developed for reducing e-bike users’ RLR rate. Electric bike (e-bike) users in China have a high red light running (RLR) rate, contributing to a large number of accidents. This paper aims to examine the psychological motivation of e-bike users’ RLR intentions. A survey questionnaire was designed employing the construct of theory of planned behavior (TPB). The survey was performed in Chengdu, China in November 2016. We found that users older than 40 identify themselves as more cautious riders. Younger riders have higher intention to run the red light. E-bike users with car drivers’ licenses regard running the red light as a more difficult task to perform, and regard this behavior as more morally wrong. Hierarchical regression was used to analyze the data. The results showed that demographic variables (age, marriage status, and college degree), TPB variables (attitude and perceived behavioral control) and extended variables (moral norm and self-identity) are significant predictors for the intention of RLR behavior. The results could provide reference for designing more effective interventions and safety education programs for reducing e-bike users’ RLR rate.