Abstract
This paper focuses on investigating electric bikers’ (e-bikers) crossing behavior and violations based on survey data of 3,126 e-bikers collected at signalized intersections in Nantong, China. We first explore e-bikers’ characteristics of late crossing, incomplete crossing, and violating crossing behaviors by frequency analysis and duration distribution, and examine a few influential factors for e-bikers’ red-light running (RLR) behavior, including site type, crossing length and traffic signal countdown timers (TSCTs). E-bikers’ RLR behavior is further divided into three categories, namely GR near-violations, RR violations, and RG violations. Second, we use a binary logistic regression model to identify the relationship between e-bikers’ RLR behavior and potential influential factors, including demographic attributes, movement information, and infrastructure conditions. We not only make regression analysis for respective violation type, but also carry out an integrated regression of a census of all three types of violations. Some insightful findings are revealed: (i) the green signal time and site type are the most significant factors to GR near-violations, but with little impact on the other two violation types; (ii) the waiting time, waiting position, passing cars and crossing length exert considerable impact on RR violations; (iii) for RG violations, TSCTs, leading violators and gender are the most significant factors; (iv) it is also unveiled that site type, green signal time and TSCTs have negligible impact on the whole violations regardless of the violation types. Thus, it is more meaningful to investigate the impacts of these variables on e-bikers’ RLR behavior according to different violation types; otherwise, the potential relationship between some crucial factors and e-bikers’ RLR behavior might be ignored. These findings would help to improve intersection crossing safety for traffic management.
Highlights
E number of e-bikes in China has been rapidly increasing, causing signi cant tra c safety issues. e total number of accidents and fatalities involving e-bikers has increased drastically in the past decade
To clarify the signal phase of e-bikers’ arriving, waiting, starting crossing, and completing crossing, the tra c signal status was divided into four phases: steady green signal (SGS), last 10 s green signal (LGS), yellow signal (YS), and red signal (RS)
SGS and last s green signal (LGS) phases are referred to as the green signal (GS) phase (Figure 3). e reason for adding an LGS phase is that a high proportion (79%) of the e-bikers who arrived during the last 10 s of the GS phase were unable to complete the crossing before the initiation of the RS phase (Table 3). us, the application of four signal phases is bene cial for the discussion on the crossing behavior and violations
Summary
Crossing width Continuous Width of straight crossing, represented by width of nonmotorized lane. If e-bikers perform a no-stopping crossing, waiting position is regarded as in designated area. In the T-intersections (see in Figure 1(b)), the e-bike ows 1 and 2 were both subject to the red light, despite the e-bike ow 2 had no con icts with passing car ows during the whole tra c signal cycle. Rightturn e-bikers were not coded as they were not subject to the tra c light following the Chinese Road Tra c Safety Law; le -turn e-bikers were excluded due to the limited eld of view of the video cameras [22, 23]. One-way intraclass correlations (for continuous variables) and Cohen’s kappa (for categorical variables) were used to validate the coding reliability. e calculated coe cients ranged from 0.85 to 0.97, indicating that the video coding was reliable
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
