Analyzing and Modeling of Multi-Class E-Bikes Violation Behaviors at Signalized Intersection

The e-bike violations have significant impacts on traffic efficiency and safety at signalized intersections, and they are important safety control objects. Based on the data of 9726 non-motor vehicles at signalized intersections obtained by video survey method, the research compared and analyzed the characteristics of violation behaviors of three types of non-motor vehicles under the influence of riders’ personal attributes and time-space scenes. Considering 14 factors as covariables, including rider’s personal attributes, signalized intersection characteristics and traffic flow characteristics, a multi-category violation model of e-bikes based on multiple Logistic regression was developed to reveal the mechanism of e-bikes running red lights, occupying motor lanes, waiting for crossing lines and reverse riding at signalized intersections. The results show that: the overall violation rate of e-bikes at signalized intersections is 44.01%, which is 1.21 times that of traditional bicycles; the model middle-aged and elderly e-bike riders are more likely to commit four kinds of violations than young ones; female cyclists are more likely to cross the line waiting and reverse cycling, while male cyclists are more likely to occupy the motorway. The addition of coordinators can effectively reduce the red light running, line crossing and reverse riding behaviors of e-bikes at signalized intersections, but might increase the possibility of e-bikes occupying the motorway. Setting the exclusive phase of left turn can effectively reduce the occupation of motor vehicle lane, line crossing waiting and reverse riding behavior of e-bikes.