The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between suppl...The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between supply and demand during daily operation,especially around the metro stations.A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs.Therefore,understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems.This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns.The results found six patterns on weekdays and four patterns on weekends.Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours,while these phenomena become less intensive on weekends.Road density,living service facility density and residential density are the top influencing factors on both weekdays and weekends,which means that the comprehensive impact of built-up environment attraction,facility suitability and riding demand leads to the different usage patterns.The nonlinear influence universally exists,and the probability of a certain pattern varies in different value ranges of variables.When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced,it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow.The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users,which could be mitigated by corresponding solutions.The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.展开更多
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supported by the National Key Research and Development Program of China[grant number 2017YFB0503601]。
文摘The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between supply and demand during daily operation,especially around the metro stations.A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs.Therefore,understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems.This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns.The results found six patterns on weekdays and four patterns on weekends.Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours,while these phenomena become less intensive on weekends.Road density,living service facility density and residential density are the top influencing factors on both weekdays and weekends,which means that the comprehensive impact of built-up environment attraction,facility suitability and riding demand leads to the different usage patterns.The nonlinear influence universally exists,and the probability of a certain pattern varies in different value ranges of variables.When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced,it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow.The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users,which could be mitigated by corresponding solutions.The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.
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.
