In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users’ traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.

Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.

Vehicle to vulnerable road user (VRU) crash is a large portion of traffic crashes in China Crash data from Beijing, China from the year 2009 to 2012 are used to identify the factors associated with the likelihood of vehicle to VRU crashes. 180 passenger-vehicles to VRU crashes are collected including 60 vehicle to pedestrian, 60 vehicle to bicycle and 60 vehicle to electric-bicycle cases. Then the statistics of the crash data are carried out, and the variables of human, vehicle, road, environment are investigated. Further, a logic regression model is established to analyse the significance of main contributing factors of these crashes. This paper describes the sample data, which includes time of incident, road user's age and gender, impact speed, crash pattern and VRU's head impact position. Also a comparative research among the three crash types is performed. According to the results, some characteristics of three crash types are different, such as the occurrence time, road position, impact speed and the impact position of VRU's head on the passenger-car. Moreover, chi-square test reveals that night-time travelling, crash type involving pedestrian and speeding of vehicle are significant related to non-fatal/fatal crashes. The logic regression model shows that night-time, intersection, older age of VRU and higher speed of vehicle increased the crash severity.