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.

The existing research on identifying black spots in traffic accidents is mostly based on accident frequency or accident rate, without considering the impact characteristics of traffic accidents on different locations. In order to comprehensively consider the differential effects of traffic accidents in different traffic environments and road network characteristics and to solve the zero inflation problem of zero values far exceeding the classical discrete distribution in traffic accident data, this paper proposed an improved network kernel density estimation method that considers the comprehensive importance of nodes, and identified urban traffic accident black spots based on the zero inflation negative binomial regression model. Firstly, in the topological road network, a comprehensive impact index of accidents was constructed by comprehensively considering the traffic environment and road conditions at the location of the accident, and the accident severity index was embedded into the traditional network kernel density estimation. By generating a smooth density surface on the road network, the spatial aggregation of point events was qualitatively reflected. On this basis, a discrimination model based on zero-inflated negative binomial regression was constructed to clarify the boundary range of accident-prone areas and quantitatively depict the spatial distribution characteristics of accident black spots at different severity levels. Finally, an example analysis was carried out for Huaqiangbei street in Shenzhen. The results show that the search efficiency indexes of the proposed method are all larger than those of the planar kernel density estimation method at the threshold levels of 70%, 80% and 90%. Furthermore, some non-road areas are no longer mistaken, and the accuracy of the model is 3.60%, 5.31% and 7.20% larger than those of the traditional network kernel density method respectively after considering the comprehensive importance of nodes.