轨道交通站点辐射范围内共享单车时空使用模式分析（英文）
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摘要
为了研究轨道交通站点辐射范围内共享单车的时空分布特性,通过分析南京一周5个工作日的共享单车出行数据,从时间和空间上分别提出了新的标准化方法分析共享单车在轨道交通站点辐射范围内不同类型的时空使用模式.首先,划定轨道交通站点辐射范围区域(RTSA)并提取其范围内的共享单车出行数据;然后,分别定义了标准化时间变化率(NDVB)和标准化空间分布率(NSDT)以测度共享单车的时间和空间使用特性;最后,对2种标准化指标值进行聚类并可视化其地理分布.结果表明,南京市轨道交通站点辐射范围内共享单车呈现出4类时间使用模式和2类空间使用模式,其中共享单车时间使用模式与轨道交通站点所在的区域类型(城市中心/郊区)密切相关;而空间使用模式受共享单车主要来车去车方向、临近轨道交通站点的分布以及周边路网密度的影响较大.研究结果有助于更好地理解轨道交通站点附近共享单车用户的接驳行为,并有利于提高轨道交通和共享单车系统的服务质量与接驳效率.
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.
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.
引文
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[2] Ji Y J,Fan Y L,Ermagun A,et al.Public bicycle as a feeder mode to rail transit in China:The role of gender,age,income,trip purpose,and bicycle theft experience[J].International Journal of Sustainable Transportation,2017,11(4):308-317.DOI:10.1080/15568318.2016.1253802.
[3] Kaltenbrunner A,Meza R,Grivolla J,et al.Urban cycles and mobility patterns:Exploring and predicting trends in a bicycle-based public transport system[J].Pervasive and Mobile Computing,2010,6(4):455-466.DOI:10.1016/j.pmcj.2010.07.002.
[4] Lin J R,Yang T H.Strategic design of public bicycle sharing systems with service level constraints[J].Transportation Research Part E:Logistics and Transportation Review,2011,47(2):284-294.DOI:10.1016/j.tre.2010.09.004.
[5] Jia Z L,Xie G,Gao J,et al.Bike-sharing system:A big-data perspective[C]//First International Conference on Smart Computing and Communication.Shenzhen,China,2016:548-557.
[6] Ji Y J,Ma X W,Yang M Y,et al.Exploring spatially varying influences on metro-bikeshare transfer:A geographically weighted poisson regression approach[J].Sustainability,2018,10(5):1526.DOI:10.3390/su10051526.
[7] Pal A,Zhang Y.Free-floating bike sharing:Solving real-life large-scale static rebalancing problems[J].Transportation Research Part C:Emerging Technologies,2017,80:92-116.DOI:10.1016/j.trc.2017.03.016.
[8] Wang J C,Ouyang S S.Disequilibrium of bicycle-sharing in rail transit station areas in Beijing[J].Journal of Transportation Systems Engineering and Information Technology,2019,19(1):214-221.DOI:10.16097/j.cnki.1009-6744.2019.01.032.(in Chinese)
[9] Froehlich J,Neumann J,Oliver N.Sensing and predicting the pulse of the city through shared bicycling[C]//Proceedings of the 21st International Joint Conference on Artificial Intelligence.Pasadena,CA,USA,2009:1420-1426.
[10] Lathia N,Ahmed S,Capra L.Measuring the impact of opening the London shared bicycle scheme to casual users[J].Transportation Research Part C:Emerging Technologies,2012,22:88-102.DOI:10.1016/j.trc.2011.12.004.
[11] O'Neil P C,Caulfield B.Examining user behaviour on a shared bike scheme:The case of Dublin bikes [C]//The 13th International Conference on Travel Behaviour Research.Toronto,Canada,2012.
[12] Chabchoub Y,Fricker C.Classification of the vélib stations using Kmeans,dynamic time wraping and DBA averaging method[C]//International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM).Paris,France,2014:14863476.DOI:10.1109/IWCIM.2014.7008802.
[13] de Chardon C M,Caruso G,Thomas I.Bike-share rebalancing strategies,patterns,and purpose[J].Journal of Transport Geography,2016,55:22-39.DOI:10.1016/j.jtrangeo.2016.07.003.