{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T12:42:21Z","timestamp":1778935341389,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,2]],"date-time":"2019-05-02T00:00:00Z","timestamp":1556755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The main objective of this study is to explore the spatiotemporal activities pattern of bicycle sharing system by combining together temporal and spatial attributes variables through clustering analysis method. Specifically, three clustering algorithms, i.e., hierarchical clustering, K-means clustering, expectation maximization clustering, are chosen to group the bicycle sharing stations. The temporal attributes variables are obtained through the statistical analysis of bicycle sharing smart card data, and the spatial attributes variables are quantified by point of interest (POI) data around bicycle sharing docking stations, which reflects the influence of land use on bicycle sharing system. According to the performance of the three clustering algorithms and six cluster validation measures, K-means clustering has been proven as the better clustering algorithm for the case of Ningbo, China. Then, the 477 bicycle sharing docking stations were clustered into seven clusters. The results show that the stations of each cluster have their own unique spatiotemporal activities pattern influenced by people\u2019s travel habits and land use characteristics around the stations. This analysis will help bicycle sharing operators better understand the system usage and learn how to improve the service quality of the existing system.<\/jats:p>","DOI":"10.3390\/info10050163","type":"journal-article","created":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T03:15:46Z","timestamp":1557198946000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Spatiotemporal Clustering Analysis of Bicycle Sharing System with Data Mining Approach"],"prefix":"10.3390","volume":"10","author":[{"given":"Xinwei","family":"Ma","sequence":"first","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiming","family":"Cao","sequence":"additional","affiliation":[{"name":"Architects &amp; Engineers Co. and LTD, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchuan","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2105\/AJPH.2012.300939","article-title":"Health Cobenefits and Transportation-Related Reductions in Greenhouse Gas Emissions in the San Francisco Bay Area","volume":"103","author":"Maizlish","year":"2013","journal-title":"Am. J. Public Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"05015011","DOI":"10.1061\/(ASCE)UP.1943-5444.0000299","article-title":"Empirical Analysis of a Mode Shift to Using Public Bicycles to Access the Suburban Metro: Survey of Nanjing, China","volume":"142","author":"Yang","year":"2016","journal-title":"J. Urban Plan. Dev."},{"key":"ref_3","unstructured":"Meddin, R., and Demaio, P.J. (2019, February 27). The Bike-Sharing World Map. Available online: www.bikesharingmap.com."},{"key":"ref_4","unstructured":"Yang Fang, M.G. (2019, February 27). Report of Shared Bicycle. Available online: http:\/\/tech.sina.com.cn\/roll\/2018-03-07\/doc-ifxtevrp244313."},{"key":"ref_5","unstructured":"Tang, Y., Pan, H., and Shen, Q. (2011, January 23\u201327). Bike-sharing systems in Beijing, Shanghai, and Hangzhou and their impact on travel behavior (No. 11-3862). Proceedings of the Transportation Research Board 90th Annual Meeting, Washington, DC, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3141\/2247-05","article-title":"China\u2019s Hangzhou Public Bicycle","volume":"2247","author":"Shaheen","year":"2011","journal-title":"Transp. Res. Record J. Transp. Res. Board"},{"key":"ref_7","first-page":"148","article-title":"Bike Share: A Synthesis of the Literature","volume":"33","author":"Fishman","year":"2013","journal-title":"Urban Transp. China"},{"key":"ref_8","unstructured":"Industry, T.I.o.C. (2019, February 27). The Information of China Industry, The Analysis of History, Status Quo and Scale of China\u2019s Public Bicycle in 2017. Available online: http:\/\/www.chyxx.com\/industry\/201704\/513812.html."},{"key":"ref_9","unstructured":"GuangZhou Planning Bureau (2019, February 27). The Case of Big Data Combined with City Planning. Available online: https:\/\/mp.weixin.qq.com\/s?__biz=MzA3OTU3ODgxNA==&mid=2650583825&idx=1&sn=4a1e2f4df6d96e0d08ba639278f1da8e&chksm=87b940c0b0cec9d65ee7a76a0d25232d7811a83ab3858052d019bfb98780669aed6d4ed67139&mpshare=1&scene=23&srcid=0628YETa2Iyklv5UMuD51Exy#rd."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Du, M., and Cheng, L. (2018). Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China. Sustainability, 10.","DOI":"10.3390\/su10041244"},{"key":"ref_11","unstructured":"Transportation, D.O. (2019, February 27). Guidance to Encourage and Regulate Bike Sharing Was Issued, Available online: http:\/\/www.gov.cn\/xinwen\/2017-08\/03\/content_5215640.htm."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.trf.2018.04.022","article-title":"Understanding bike-sharing acceptability and expected usage patterns in the context of a small city novel to the concept: A story of \u2018Greek Drama\u2019","volume":"56","author":"Nikitas","year":"2018","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_13","unstructured":"Froehlich, J.E., Neumann, J., and Oliver, N. (2009, January 17). Sensing and predicting the pulse of the city through shared bicycling. Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, Pasadena, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jtrangeo.2016.07.003","article-title":"Bike-share rebalancing strategies, patterns, and purpose","volume":"55","author":"Caruso","year":"2016","journal-title":"J. Transp. Geogr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1080\/01441647.2015.1033036","article-title":"Bikeshare: A Review of Recent Literature","volume":"36","author":"Fishman","year":"2016","journal-title":"Transp. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3141\/2387-10","article-title":"Public Bikesharing in North America: Early Operator Understanding and Emerging Trends","volume":"1568","author":"Shaheen","year":"2013","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_17","first-page":"28","article-title":"Bike sharing: A review of evidence on impacts and processes of implementation and operation","volume":"15","author":"Ricci","year":"2015","journal-title":"Res. Transp. Bus. Manag."},{"key":"ref_18","first-page":"42","article-title":"Survey on the reorganization and using status of public bicycle system in urban fringe areas: Taking Tongzhou and Daxing Districts of Beijing for example","volume":"3","author":"Zhang","year":"2015","journal-title":"Urban Probl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fishman, E., Washington, S., and Haworth, N. (2013). Erratum to Bike share: A synthesis of the literature (Transport Reviews). Transp. Rev., 33.","DOI":"10.1080\/01441647.2013.775612"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.pmcj.2010.07.002","article-title":"Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system","volume":"6","author":"Kaltenbrunner","year":"2010","journal-title":"Pervasive Mob. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jtrangeo.2014.05.011","article-title":"A study of acceptable trip distances using walking and cycling in Bangalore","volume":"38","author":"Rahul","year":"2014","journal-title":"J. Transp. Geogr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.trc.2015.01.030","article-title":"Exploring bikesharing travel time and trip chain by gender and day of the week","volume":"58","author":"Zhao","year":"2015","journal-title":"Transp. Res. Part C"},{"key":"ref_23","unstructured":"Li, X., Zhang, Y., Zhang, R., Lu, Y., and Xie, S. (2018). Overcoming Barriers to Cycling: Exploring Influence Factors of Cyclists\u2019 Preference in Free-Floating Bikesharing, The National Academies of Sciences, Engineering, and Medicine. Technical Report No. 18-02707."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0969-6989(02)00036-X","article-title":"Card loyalty. A new emerging issue in grocery retailing","volume":"10","author":"Mauri","year":"2003","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1142\/S0219525911002950","article-title":"Shared bicycles in a city: A signal processing and data analysis perspective","volume":"14","author":"Borgnat","year":"2011","journal-title":"Adv. Complex Syst."},{"key":"ref_26","unstructured":"Come, E., Randriamanamihaga, N.A., Oukhellou, L., and Aknin, P. (2014, January 12\u201316). Spatio-temporal analysis of dynamic origin-destination data using latent dirichlet allocation: Application to v\u00e9lib\u2019bike sharing system of paris. Proceedings of the TRB 93rd Annual Meeting, Washington, DC, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Austwick, M.Z., O\u2019Brien, O., Strano, E., and Viana, M. (2013). The structure of spatial networks and communities in bicycle sharing systems. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0074685"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.jtrangeo.2013.06.007","article-title":"Mining bicycle sharing data for generating insights into sustainable transport systems","volume":"34","author":"Cheshire","year":"2014","journal-title":"J. Transp. Geogr."},{"key":"ref_29","unstructured":"System, C.o.N.B.S. (2019, February 27). Ningbo Public Bicycle Service Development Co. Operational Information. Available online: http:\/\/www.nbbicycle.com."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.sbspro.2011.08.058","article-title":"Understanding bike-sharing systems using data mining: Exploring activity patterns","volume":"20","author":"Vogel","year":"2011","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yahya, B. (2017). Overall bike effectiveness as a sustainability metric for bike sharing systems. Sustainability, 9.","DOI":"10.3390\/su9112070"},{"key":"ref_32","unstructured":"Xu, C., Wang, Y., Wang, C., and Liu, P. (2019, January 13\u201317). Investigation of Contributing Factors to Travel Demand of Free-floating Bike Sharing: A Geographically Weighted Regression Approach (No. 19-03556). Proceedings of the Transportation Research Board 98th Annual Meeting, Washington, DC, USA."},{"key":"ref_33","unstructured":"Corporation, B. (2019, February 27). Classification of Poi Data. Available online: http:\/\/lbsyun.baidu.com\/index.php?title=lbscloud\/poitags."},{"key":"ref_34","unstructured":"Wu, C., Inhi, K., and Hyungchul, C. (2019, January 13\u201317). A geographically weighted regression model to explore the relationship between built 1 environment and public sharing bike flow: Evidence from Suzhou, China. Proceedings of the TRB 98th Annual Meeting, Washington, DC, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","article-title":"Hierarchical grouping to optimize an objective function","volume":"58","author":"Ward","year":"1963","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_36","unstructured":"Teboulle, M., Berkhin, P., Dhillon, I., Guan, Y., and Kogan, J. (2006). Clustering with Entropy-Like K-Means Algorithms, Springer."},{"key":"ref_37","first-page":"100","article-title":"Algorithm AS 136: A K-Means Clustering Algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Soc."},{"key":"ref_38","unstructured":"Tan, P.N. (2018). Introduction to Data Mining, Pearson Education India. Available online: https:\/\/www-users.cs.umn.edu\/~kumar001\/dmbook\/sol.pdf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3201","DOI":"10.1093\/bioinformatics\/bti517","article-title":"Computational cluster validation in post-genomic data analysis","volume":"21","author":"Handl","year":"2005","journal-title":"Bioinformatics"},{"key":"ref_40","unstructured":"Brock, G., Pihur, V., Datta, S., and Datta, S. (2011). clValid, an R package for cluster validation. J. Stat. Softw., Available online: https:\/\/cran.microsoft.com\/web\/packages\/clValid\/vignettes\/clValid.pdf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01969727408546059","article-title":"Well separated clusters and fuzzy partitions","volume":"4","author":"Dunn","year":"1974","journal-title":"J. Cybern."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1093\/bioinformatics\/btg025","article-title":"Comparisons and validation of statistical clustering techniques for microarray gene expression data","volume":"19","author":"Datta","year":"2003","journal-title":"Bioinformatics"},{"key":"ref_44","first-page":"431","article-title":"Geographically weighted regression","volume":"47","author":"Brunsdon","year":"1998","journal-title":"J. R. Stat. Soc. Ser. D"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s101090200081","article-title":"Analysing regional industrialisation in Jiangsu province using geographically weighted regression","volume":"4","author":"Huang","year":"2002","journal-title":"J. Geogr. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1177\/0894439307298925","article-title":"Using geographically weighted regression to explore local crime patterns","volume":"25","author":"Cahill","year":"2007","journal-title":"Soc. Sci. Comput. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.cities.2018.01.017","article-title":"Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China","volume":"77","author":"Wu","year":"2018","journal-title":"Cities"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/S0169-5347(99)01607-9","article-title":"Spatial autocorrelations","volume":"14","author":"Bebber","year":"1999","journal-title":"Trends Ecol. Evol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.aap.2017.06.012","article-title":"Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas","volume":"106","author":"Bao","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.1538-4632.1992.tb00261.x","article-title":"The Analysis of Spatial Association by Use of Distance Statistics","volume":"24","author":"Getis","year":"1992","journal-title":"Geogr. Anal."},{"key":"ref_51","unstructured":"Charlton, M., and Fotheringham, A. (2019, February 27). Geographically Weighted Regression (White Paper). Available online: https:\/\/www.geos.ed.ac.uk\/~gisteac\/fspat\/gwr\/gwr_arcgis\/GWR_WhitePaper.pdf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1538-4632.1996.tb00936.x","article-title":"Geographically weighted regression: A method for exploring spatial nonstationarity","volume":"28","author":"Brunsdon","year":"1996","journal-title":"Geogr. Anal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.apgeog.2015.02.011","article-title":"Spatial variation of the urban taxi ridership using GPS data","volume":"59","author":"Qian","year":"2015","journal-title":"Appl. Geogr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1111\/j.0002-9092.2004.600_2.x","article-title":"Geographically Weighted Regression: The Analysis of Spatially Varying Relationships","volume":"86","author":"Mcmillen","year":"2004","journal-title":"Am. J. Agric. Econ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.trd.2018.11.021","article-title":"Factors influencing trip generation on metro system in Madrid (Spain)","volume":"67","author":"Calvo","year":"2019","journal-title":"Transp. Res. Part D Transp. Environ."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/5\/163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:48:48Z","timestamp":1760186928000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/5\/163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,2]]},"references-count":55,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["info10050163"],"URL":"https:\/\/doi.org\/10.3390\/info10050163","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,2]]}}}