Severity analyses and prediction of e-bikes violated behaviors
Traffic conflicts caused by illegal riding of e-bikes are a great challenge and negative impact on the safety management and operation efficiency of signalized intersections.In this paper,three indexes including post-encroachment time(PET),time to collision(TTC)and deceleration-to-safety time(DST)were selected from the two aspects of the number of collision objects and motion state.The k-means clustering was adopted to divide the severity of traffic collisions caused by illegal riding of e-bikes into three categories:general,serious and potential collision.Secondly,the Poisson function was used to fit the distribution characteristics of conflict frequency,random variables were introduced to describe the mixed effects of heterogeneity among traffic conflicts,and a prediction model for traffic conflicts of illegal riding e-bikes based on GLMM was built to predict the frequency of traffic conflicts of multi-grade severity.Combined with the data of 996 e-bike traffic conflicts obtained by video investigation,the empirical study shows that the proportion of e-bike traffic conflicts with different severity has nothing to do with the types of violations.The constructed GLMM model is better than generalized linear model(GLM)in fitting the traffic conflict data of illegal cycling e-bikes,and has the best prediction effect on the common conflict frequency.By strengthening the management of e-bike occupation of motor vehicle lanes and the waiting behavior of crossing the line,adding escort officers and adjusting the signal phase,the incidence rate of e-bike conflict can be reduced.
e-bikesgeneralized linear mixed model(GLMM)violated behaviorsseverity levelconflict prediction
万方数据
维普
