کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7257537 | 1472429 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Predicting e-bike users' intention to run the red light: An application and extension of the theory of planned behavior
ترجمه فارسی عنوان
پیش بینی استفاده کنندگان از وسایل الکترونیکی دوچرخه برای اجرای نور قرمز: برنامه کاربردی و گسترش نظریه رفتار برنامه ریزی شده
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کلمات کلیدی
الکتریک دوچرخه، نقض ترافیک، مدل روانشناسی اجتماعی، خودرو غیر مرسوم، رفتار سواری،
موضوعات مرتبط
علوم انسانی و اجتماعی
روانشناسی
روان شناسی کاربردی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Transportation Research Part F: Traffic Psychology and Behaviour - Volume 58, October 2018, Pages 282-291
Journal: Transportation Research Part F: Traffic Psychology and Behaviour - Volume 58, October 2018, Pages 282-291
نویسندگان
Hongtai Yang, Xiaohan Liu, Fan Su, Christopher Cherry, Yugang Liu, Yanlai Li,