Journal of safety research · 2025
Development and validation of a video-based hazard prediction test for e-bike riders in China.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Sun Long, Zhang Meiqi, Ma Yunong
Аннотация
PURPOSE: The present study aimed to develop a hazard prediction test for e-bike riders using the hazard prediction paradigm. The validity of the test was examined by comparing its results with retrospective traffic accidents in the preceding three years and with prospective traffic accidents in the 12 months following the survey.
METHOD: Study 1 employed a single-factor experimental design and presented 28 video clips to 60 experienced riders and 60 novice riders. Study 2 examined the known group validity of the newly developed test by comparing the differences in the test scores of e-bike riders who had been involved in accidents in the preceding three years (n = 53) and those who had not (n = 59) and examined the predictive validity of the test using negative binomial regression analysis by exploring whether the test score could predict accident involvement in the following 12 months.
RESULTS: The results of Study 1 show that the newly developed hazard prediction test contains 20 videos with satisfactory reliability (Cronbach's alpha = 0.78), and experienced e-bike riders scored significantly higher than novices did on the test. The results of Study 2 show that e-bike riders involved in accidents in the preceding three years had lower test scores than did those not involved in accidents. More importantly, the test scores effectively predicted the crash risk of e-bike riders in the following 12 months, suggesting that the predictive validity of the test is satisfactory.
CONCLUSIONS: The hazard prediction test for e-bike riders has acceptable reliability and validity and can be used as a reliable tool to assess the hazard perception of e-bike riders.
METHOD: Study 1 employed a single-factor experimental design and presented 28 video clips to 60 experienced riders and 60 novice riders. Study 2 examined the known group validity of the newly developed test by comparing the differences in the test scores of e-bike riders who had been involved in accidents in the preceding three years (n = 53) and those who had not (n = 59) and examined the predictive validity of the test using negative binomial regression analysis by exploring whether the test score could predict accident involvement in the following 12 months.
RESULTS: The results of Study 1 show that the newly developed hazard prediction test contains 20 videos with satisfactory reliability (Cronbach's alpha = 0.78), and experienced e-bike riders scored significantly higher than novices did on the test. The results of Study 2 show that e-bike riders involved in accidents in the preceding three years had lower test scores than did those not involved in accidents. More importantly, the test scores effectively predicted the crash risk of e-bike riders in the following 12 months, suggesting that the predictive validity of the test is satisfactory.
CONCLUSIONS: The hazard prediction test for e-bike riders has acceptable reliability and validity and can be used as a reliable tool to assess the hazard perception of e-bike riders.