title: The red-light running behavior of electric bike riders and cyclists at urban intersections in China: an observational study.
abstract: Electric bikes and regular bicycles play an important role in the urban transportation system of China. Red-light running is a type of highly dangerous behavior of two-wheeled riders. The main purpose of this study was to investigate the rate, associated factors, and behavior characteristics of two-wheelers' red-light running in China. A field observational study was conducted using two synchronized video cameras at three signalized intersections in Beijing. A total of 451 two-wheelers facing a red light (222 e-bike riders and 229 cyclists) were observed and analyzed. The results showed that 56% of the two-wheelers crossed the intersection against a red light. Age was found to be a significant variable for predicting red-light runners, with the young and middle-aged riders being more likely than the old ones to run against a red light. The logistic regression analysis also indicated that the probability of a rider running a red light was higher when she or he was alone, when there were fewer riders waiting, and when there were riders already crossing on red. Further analysis of crossing behavior revealed that the majority of red-light running occurred in the early and late stages of a red-light cycle. Two-wheelers' crossing behavior was categorized into three distinct types: law-obeying (44%), risk-taking (31%) and opportunistic (25%). Males were more likely to act in a risk-taking manner than females, and so were the young and middle-aged riders than the old ones. These findings provide valuable insights in understanding two-wheelers' red-light running behaviors, and their implications in improving road safety were discussed.
authors.affiliation: State University of New York at Buffalo, Industrial and Systems Engineering, 414 Bell Hall, Buffalo, United States. changxu.wu@gmail.com
authors.forename: Changxu
authors.initials: C
authors.lastname: Wu
authors.forename: Lin
authors.initials: L
authors.lastname: Yao
authors.forename: Kan
authors.initials: K
authors.lastname: Zhang
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001642:Bicycling / Q000331:legislation & jurisprudence / Q000523:psychology*; D002681:China; D003415:Crime / Q000523:psychology* / Q000706:statistics & numerical data; D003430:Cross-Sectional Studies; D003617:Dangerous Behavior; D055615:Electrical Equipment and Supplies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012449:Safety / Q000331:legislation & jurisprudence*; D012737:Sex Factors; D013997:Time Factors; D014505:Urban Population; D014741:Video Recording; D055815:Young Adult