{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:11:59Z","timestamp":1760209919170,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T00:00:00Z","timestamp":1508716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Analyzing public movement in transportation networks in a city is significant in understanding the life of citizen and making improved city plans for the future. This study focuses on investigating the flow orientation of major activity regions based on smart card transit data. The flow orientation based on the real movements such as transit data can provide the easiest way of understanding public movement in the complicated transportation networks. First, high inflow regions (HIRs) are identified from transit data for morning and evening peak hours. The morning and evening HIRs are used to represent major activity regions for major daytime activities and residential areas, respectively. Second, the directional orientation of flow is then derived through the directional inflow vectors of the HIRs to show the bias in directional orientation and compare flow orientation among major activity regions. Finally, clustering analysis for HIRs is applied to capture the main patterns of flow orientations in the city and visualize the patterns on the map. The proposed methodology was illustrated with smart card transit data of bus and subway transportation networks in Seoul, Korea. Some remarkable patterns in the distribution of movements and orientations were found inside the city. The proposed methodology is useful since it unfolds the complexity and makes it easy to understand the main movement patterns in terms of flow orientation.<\/jats:p>","DOI":"10.3390\/ijgi6100318","type":"journal-article","created":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T12:20:24Z","timestamp":1508761224000},"page":"318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data"],"prefix":"10.3390","volume":"6","author":[{"given":"Parul","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin, Gyeonggi 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyuhyup","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin, Gyeonggi 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae-Yoon","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin, Gyeonggi 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1111\/0022-4146.00071","article-title":"A nonparametric analysis of employment density in a polycentric city","volume":"37","author":"McMillen","year":"1997","journal-title":"J. Reg. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1111\/1467-940X.00051","article-title":"Evolution of employment centers in Seoul","volume":"14","author":"Jun","year":"2002","journal-title":"Rev. Urban Reg. Dev. Stud."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1111\/j.1538-4632.2004.tb01130.x","article-title":"Spatial analysis of employment and population density: the case of the agglomeration of Dijon 1999","volume":"36","author":"Baumont","year":"2004","journal-title":"Geogr. Anal."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Roth, C., Kang, S.M., Batty, M., and Barth\u00e9lemy, M. (2011). Structure of urban movements: polycentric activity and entangled hierarchical flows. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0015923"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1080\/13658816.2014.914521","article-title":"Detecting the dynamics of urban structure through spatial network analysis","volume":"28","author":"Zhong","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1111\/jors.12208","article-title":"Empirical polycentricity: The complex relationship between employment centers","volume":"56","author":"Craig","year":"2016","journal-title":"J. Reg. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, X., Fang, Z., Xu, Y., Shaw, S.L., Zhao, Z., Yin, L., and Lin, Y. (2016). Understanding spatiotemporal patterns of human convergence and divergence using mobile phone location data. ISPRS Int. J. Geo-Inf.","DOI":"10.3390\/ijgi5100177"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0166-0462(91)90036-M","article-title":"Urban subcenter formation","volume":"21","author":"Helsley","year":"1991","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_9","unstructured":"Geohash (2017, May 01). Available online: http:\/\/Geohash.org\/site\/tips.html."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.trc.2010.12.003","article-title":"Smart card data use in public transit: A literature review","volume":"19","author":"Pelletier","year":"2011","journal-title":"Transp. Res. C Emer. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Morency, C., Tr\u00e9panier, M., and Agard, B. (2006, January 17\u201320). Analysing the Variability of Transit Users\u2019 Behaviour with Smart Card Data. Proceedings of the 19th International IEEE Intelligent Transportation Systems Conference (ITSC), Toronto, ON, Canada.","DOI":"10.1109\/ITSC.2006.1706716"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.tranpol.2007.01.001","article-title":"Measuring transit use variability with smart-card data","volume":"14","author":"Morency","year":"2007","journal-title":"Transp. Policy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trc.2013.07.010","article-title":"Mining smart card data for transit riders\u2019 travel patterns","volume":"36","author":"Ma","year":"2013","journal-title":"Transp. Res. C Emer. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/TITS.2014.2368998","article-title":"Passenger segmentation using smart card data","volume":"16","author":"Kieu","year":"2015","journal-title":"IEEE Trans. Intell. Transp."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1080\/13658816.2014.898768","article-title":"An analysis on movement patterns between zones using smart card data in subway networks","volume":"28","author":"Kim","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Du, B., Yang, Y., and Lv, W. (2013, January 3\u20136). Understand Group Travel Behaviors in an Urban Area Using Mobility Pattern Mining. Proceedings of the 10th IEEE International Conference on Ubiquitous Intelligence and Computing and 10th International Conference on Autonomic and Trusted Computing (UIC\/ATC), Washington, DC, USA.","DOI":"10.1109\/UIC-ATC.2013.64"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.apgeog.2014.06.008","article-title":"Exploring Bus Rapid Transit passenger travel behaviour using big data","volume":"53","author":"Tao","year":"2014","journal-title":"App. Geogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jtrangeo.2014.08.006","article-title":"Examining the spatial\u2013temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap","volume":"41","author":"Tao","year":"2014","journal-title":"J. Transp. Geogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compenvurbsys.2015.02.005","article-title":"Combining smart card data and household travel survey to analyze jobs\u2013housing relationships in Beijing","volume":"53","author":"Long","year":"2015","journal-title":"Comp. Environ. Urban Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1109\/TITS.2016.2639320","article-title":"Visualizing the Relationship Between Human Mobility and Points of Interest","volume":"18","author":"Zeng","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compenvurbsys.2014.07.004","article-title":"Inferring building functions from a probabilistic model using public transportation data","volume":"48","author":"Zhong","year":"2014","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.tranpol.2005.06.008","article-title":"The potential of public transport smart card data","volume":"12","author":"Bagchi","year":"2005","journal-title":"Transp. Policy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.trc.2014.05.012","article-title":"Behavioural data mining of transit smart card data: A data fusion approach","volume":"46","author":"Kusakabe","year":"2014","journal-title":"Transp. Res. C Emer Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jtrangeo.2015.08.005","article-title":"Identification and classification of public transport activity centres in Stockholm using passenger flows data","volume":"48","author":"Cats","year":"2015","journal-title":"J. Transp. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/tgis.12100","article-title":"Mapping large spatial flow data with hierarchical clustering","volume":"18","author":"Zhu","year":"2014","journal-title":"Trans. GIS"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s10109-011-0150-z","article-title":"Industrial agglomeration and transport accessibility in metropolitan Seoul","volume":"14","author":"Song","year":"2012","journal-title":"J. Geogr. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/TVCG.2015.2467194","article-title":"Telcovis: Visual exploration of co-occurrence in urban human mobility based on telco data","volume":"22","author":"Wu","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1109\/TVCG.2012.311","article-title":"Scalable analysis of movement data for extracting and exploring significant places","volume":"19","author":"Andrienko","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MPRV.2014.30","article-title":"Mining private information from public data: The Transantiago Case","volume":"13","author":"Bahamonde","year":"2014","journal-title":"IEEE Pervas. Comp."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ma, Y., Xu, W., Zhao, X., and Li, Y. (2017). Modeling the hourly distribution of population at a high spatiotemporal resolution using subway smart card data: A case study in the central area of Beijing. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050128"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1108\/eb024706","article-title":"The Pareto principle: Its use and abuse","volume":"1","author":"Sanders","year":"1987","journal-title":"J. Serv. Mark."},{"key":"ref_32","unstructured":"Juran, J.M., and Gryna, F.M. (1998). Juran\u2019s Quality Control Handbook, McGraw-Hill. [5th ed.]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1016\/0167-8191(95)00017-I","article-title":"Parallel algorithms for hierarchical clustering","volume":"21","author":"Olson","year":"1995","journal-title":"Parallel Comput."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/10\/318\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:14Z","timestamp":1760208494000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/10\/318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,23]]},"references-count":33,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["ijgi6100318"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6100318","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2017,10,23]]}}}