Ruikang Yu1, Hui Zhao1, Cuiping Zhang2, Ziyang Wang1. 1. a MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation , Beijing Jiaotong University , Beijing , China. 2. b School of Computer Science, Beijing Information Science & Technology University , Beijing , China.
Abstract
OBJECTIVE: The primary objective of this study is to understand electric bicycle riders' responses to pedestrian countdown signal devices (PCSDs), in particular, red light violations (RLVs) and early start behaviors. METHODS: Descriptive analyses were performed to compare the frequency of electric bicycle riders' risk-taking behaviors, including RLVs and early start maneuvers at signalized intersections with PCSDs and intersections without PCSDs. Binary logit models were deployed to evaluate the influence of the associated factors, including gender, presence of a pillion passenger, type of electric bicycle, and number of turning vehicles, on the RLV behaviors and early start maneuvers of electric bicycle riders at signalized intersections with PCSDs. RESULTS: Descriptive analyses indicate that PCSDs are effective in reducing the number of red light running violations. Significant variables for RLV behaviors include being female, no pillion passengers, type of electric bicycle, and number of turning vehicles. In addition, a similar correlation between early start maneuvers and influential factors was observed. CONCLUSIONS: PCSDs are effective in preventing RLVs but increase the probability of early starts. Based on the findings in this article, practical countermeasures such as redesigning PCSDs could help improve safety at intersections for electric bicycle riders and should be further investigated.
OBJECTIVE: The primary objective of this study is to understand electric bicycle riders' responses to pedestrian countdown signal devices (PCSDs), in particular, red light violations (RLVs) and early start behaviors. METHODS: Descriptive analyses were performed to compare the frequency of electric bicycle riders' risk-taking behaviors, including RLVs and early start maneuvers at signalized intersections with PCSDs and intersections without PCSDs. Binary logit models were deployed to evaluate the influence of the associated factors, including gender, presence of a pillion passenger, type of electric bicycle, and number of turning vehicles, on the RLV behaviors and early start maneuvers of electric bicycle riders at signalized intersections with PCSDs. RESULTS: Descriptive analyses indicate that PCSDs are effective in reducing the number of red light running violations. Significant variables for RLV behaviors include being female, no pillion passengers, type of electric bicycle, and number of turning vehicles. In addition, a similar correlation between early start maneuvers and influential factors was observed. CONCLUSIONS: PCSDs are effective in preventing RLVs but increase the probability of early starts. Based on the findings in this article, practical countermeasures such as redesigning PCSDs could help improve safety at intersections for electric bicycle riders and should be further investigated.
Keywords:
Electric bicycle; binary logit regression; early start; intersection; pedestrian countdown signal device; red light violation