Abstract: For the sake of studying the characteristics of traffic conflicts involving non-motor vehicles at city road intersections, univariate and multivariate regression analyses were conducted between the numbers of straight-go non-motor vehicles, right-turn motor vehicles, equivalent cars after converting and the number of conflicts of motors with non-motor vehicles per unit time in the observation time according to actual operation characteristics of non-motor vehicles at a typical intersection in Hohhot. In order to reflect the severity of conflicts involving non-motor vehicles comprehensively and accurately, severity models considering and normalizing the influences of distance, angle and speed of conflict on the severity were built. Results show that the quadratic polynomial fitted with the number of straight-go non-motor vehicles and the number of conflicts has the best predictive performance among the 9 univariate regression models and its forecast accuracy of conflicts number is 85.3%,that the function fitted with the number of straight-go non-motor vehicles, the number of equivalent cars and the number of conflicts has the best predictive performance among the 4 multivariate regression models and its forecast accuracy is 92.4%, so it is the best forecast function of conflicts number,and that the established severity models for non-motor vehicle conflicts can be used to measure the severity of traffic conflict in practice.

Key words: traffic conflicts, non-motor vehicle, regression analysis, conflict forecast model, severity of conflicts