|Table of Contents|

[1] Ji Yanjie, Cao Yu, Liu Yang, Ma Xinwei, et al. Analysis of temporal and spatial usage patternsof dockless bike sharing system around rail transit station area [J]. Journal of Southeast University (English Edition), 2019, 35 (2): 228-235. [doi:10.3969/j.issn.1003-7985.2019.02.013]

Analysis of temporal and spatial usage patternsof dockless bike sharing system around rail transit station area()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2019 2
Research Field:
Traffic and Transportation Engineering
Publishing date:


Analysis of temporal and spatial usage patternsof dockless bike sharing system around rail transit station area
Ji Yanjie Cao Yu Liu Yang Ma Xinwei
School of Transportation, Southeast University, Nanjing 211189, China
dockless bike sharing system rail transit station usage pattern cluster
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.


[1] Parkes S D, Marsden G, Shaheen S A, et al. Understanding the diffusion of public bikesharing systems: Evidence from Europe and North America[J]. Journal of Transport Geography, 2013, 31: 94-103. DOI:10.1016/j.jtrangeo.2013.06.003.
[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.


Biography: Ji Yanjie(1980—), female, doctor, associate professor, jiyanjie@seu.edu.cn.
Foundation items: 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).
Citation: Ji Yanjie, Cao Yu, Liu Yang, et al.Analysis of temporal and spatial usage patterns of dockless bike sharing system around rail transit station area[J].Journal of Southeast University(English Edition), 2019, 35(2):228-235.DOI:10.3969/j.issn.1003-7985.2019.02.013.
Last Update: 2019-06-20