Analysis of influencing factors of speeding risk on road segments for shared electric bikes
In order to explore the mechanism of influencing factors of the electric bike(e-bike)speeding be-havior,the global positioning system(GPS)trajectory data of shared e-bikes is used to realize identification and risk classification of speeding behavior.Considering characteristics such as land use,roads,and traffic status,a model that identifies speeding risk on road segments for shared e-bikes is created based on machine learning algorithms.Then,a partial dependency plot is employed to analyze the influence of each influencing factor on speeding risk on road segments.The results show that the CatBoost is better for speeding risk identi-fication on road segments than the random forest model.As land use density and curb parking density decrease and bus line density,road level,sidewalk width,and non-motorized lane width increase,the speeding risk on road segments for shared e-bikes increases.In addition,one-way roads,non-physically separated sidewalks,non-physically separated non-motorized lanes,and non-peak hours are positively associated with speeding risk on road segments.This study provides a novel method for identifying and analyzing risky e-bike behavior and technical support for non-motorized traffic safety management.
electric bikespeeding risk on road segmentstrajectory datamachine learninginfluence factor analysis
万方数据
维普
