In order to study the spatiotemporal characteristics of the dockless bike sharing system(BSS)around urban rail transit stations,new normalized calculation methods are proposed to explore the temporal and spatial usage...In order to study the spatiotemporal characteristics of the dockless bike sharing system(BSS)around urban rail transit stations,new normalized calculation methods are proposed to explore the temporal and spatial usage patterns of the dockless BSS around rail transit stations by using 5-weekday dockless bike sharing trip data in Nanjing,China.First,the rail transit station area(RTSA)is defined by extracting shared bike trips with trip ends falling into the area.Then,the temporal and spatial decomposition methods are developed and two criterions are calculated,namely,normalized dynamic variation of bikes(NDVB)and normalized spatial distribution of trips(NSDT).Furthermore,the temporal and spatial usage patterns are clustered and the corresponding geographical distributions of shared bikes are determined.The results show that four temporal usage patterns and two spatial patterns of dockless BSS are finally identified.Area type(urban center and suburb)has a great influence on temporal usage patterns.Spatial usage patterns are irregular and affected by limited directions,adjacent rail transit stations and street networks.The findings can help form a better understanding of dockless shared bike users behavior around rail transit stations,which will contribute to improving the service and efficiency of both rail transit and BSS.展开更多
基金The National Key R&D Program of China(No.2018YFB1600900)the Project of International Cooperation and Exchange of the National Natural Science Foundation of China(No.51561135003)the Key Project of National Natural Science Foundation of China(No.51338003)
文摘In order to study the spatiotemporal characteristics of the dockless bike sharing system(BSS)around urban rail transit stations,new normalized calculation methods are proposed to explore the temporal and spatial usage patterns of the dockless BSS around rail transit stations by using 5-weekday dockless bike sharing trip data in Nanjing,China.First,the rail transit station area(RTSA)is defined by extracting shared bike trips with trip ends falling into the area.Then,the temporal and spatial decomposition methods are developed and two criterions are calculated,namely,normalized dynamic variation of bikes(NDVB)and normalized spatial distribution of trips(NSDT).Furthermore,the temporal and spatial usage patterns are clustered and the corresponding geographical distributions of shared bikes are determined.The results show that four temporal usage patterns and two spatial patterns of dockless BSS are finally identified.Area type(urban center and suburb)has a great influence on temporal usage patterns.Spatial usage patterns are irregular and affected by limited directions,adjacent rail transit stations and street networks.The findings can help form a better understanding of dockless shared bike users behavior around rail transit stations,which will contribute to improving the service and efficiency of both rail transit and BSS.
