{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:34:28Z","timestamp":1760236468975,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhi Cai","award":["No.4212016"],"award-info":[{"award-number":["No.4212016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The urban rail transit stations are an important part of an urban transit system. Scientific and reasonable location of rail transit station can greatly alleviate traffic pressure. The number of people in the surrounding area of a rail transit station is an important factor for site selection. However, it is difficult to obtain the spatial distribution of population, which brings great difficulties in terms of site selection. Due to the large-scale popularization of AP (Access Point) in China, the spatial distribution of AP is used instead of population distribution to assist site selection. Therefore, a density visualization method based on a dynamic grid is proposed, which can help decision-makers intuitively see the AP density of the uncovered grid of rail transit stations, and then cluster the AP density of the uncovered area to predict the location of new rail transit stations. The validity of the proposed method is demonstrated by using the AP dataset and rail transit data of Beijing in 2013. The results show that our method has high accuracy in predicting the location of rail transit stations. It can provide data support for urban traffic development and management.<\/jats:p>","DOI":"10.3390\/ijgi10120804","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T23:22:28Z","timestamp":1638314548000},"page":"804","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Dynamic Grid-Based Spatial Density Visualization and Rail Transit Station Prediction"],"prefix":"10.3390","volume":"10","author":[{"given":"Zhi","family":"Cai","sequence":"first","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meilin","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Mi","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6305-5565","authenticated-orcid":false,"given":"Bowen","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4214-3363","authenticated-orcid":false,"given":"Xing","family":"Su","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limin","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Computer Science, Beijing University of Technology, Beijing 100124, China"},{"name":"Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, Chinese Academy of Sciences, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tra.2013.04.002","article-title":"How do transport infrastructure and policies affect house prices and rents? Evidence from Athens, Greece","volume":"52","author":"Efthymiou","year":"2013","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.jtrangeo.2010.02.006","article-title":"Urban rail systems investments: An analysis of the impacts on property values and residents\u2019 location","volume":"19","author":"Pagliara","year":"2011","journal-title":"J. Transp. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s40864-017-0054-4","article-title":"Rail Transportation Lead Urban Form Change: A Case Study of Beijing","volume":"3","author":"Zhang","year":"2017","journal-title":"Urban Rail Transit"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2018.01.001","article-title":"Human mobility: Models and applications","volume":"734","author":"Barbosa","year":"2018","journal-title":"Phys. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.regsciurbeco.2014.02.002","article-title":"Public transit and urban redevelopment: The effect of light rail transit on land use in Minneapolis, Minnesota","volume":"46","author":"Hurst","year":"2014","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Michalopoulou, M., Riihij\u00e4rvi, J., and M\u00e4h\u00f6nen, P. (2010, January 6\u201310). Studying the Relationships between Spatial Structures of Wireless Networks and Population Densities. Proceedings of the 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, Miami, FL, USA.","DOI":"10.1109\/GLOCOM.2010.5683831"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/LCOMM.2015.2509074","article-title":"A Simple WiFi Hotspot Model for Cities","volume":"20","author":"Seufert","year":"2016","journal-title":"IEEE Commun. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1109\/TVCG.2013.228","article-title":"Visual Traffic Jam Analysis Based on Trajectory Data","volume":"19","author":"Wang","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1109\/TVCG.2014.2346893","article-title":"Visualizing Mobility of Public Transportation System","volume":"20","author":"Zeng","year":"2014","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/TVCG.2015.2440259","article-title":"AllAboard: Visual Exploration of Cellphone Mobility Data to Optimise Public Transport","volume":"22","author":"Sbodio","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/TVCG.2015.2467619","article-title":"Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data","volume":"22","author":"Chen","year":"2016","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TBDATA.2016.2586447","article-title":"Visual Analytics in Urban Computing: An Overview","volume":"2","author":"Zheng","year":"2016","journal-title":"IEEE Trans. Big Data"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1109\/TITS.2015.2436897","article-title":"A Survey of Traffic Data Visualization","volume":"16","author":"Chen","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, L., Wang, S., Cai, T., and Zhao, W. (2020, January 11\u201314). VABD: Visual Analysis of Spatio-temporal Trajectory Based on Urban Bus Data. Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC51575.2020.9345182"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TVCG.2018.2864503","article-title":"Visual Abstraction of Large Scale Geospatial Origin-Destination Movement Data","volume":"25","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.4113\/jom.2010.1071","article-title":"Treemap Cartography for showing Spatial and Temporal Traffic Patterns","volume":"6","author":"Slingsby","year":"2012","journal-title":"J. Maps"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TBDATA.2016.2546301","article-title":"Visual Exploration of Changes in Passenger Flows and Tweets on Mega-City Metro Network","volume":"2","author":"Itoh","year":"2016","journal-title":"IEEE Trans. Big Data"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TVCG.2012.265","article-title":"Stacking-Based Visualization of Trajectory Attribute Data","volume":"18","author":"Tominski","year":"2012","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"166472","DOI":"10.1109\/ACCESS.2020.3021684","article-title":"Online Clustering of Evolving Data Streams Using a Density Grid-Based Method","volume":"8","author":"Tareq","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Brown, D., Japa, A., and Shi, Y. (2019, January 7\u20139). A Fast Density-Grid Based Clustering Method. Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2019.8666548"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Edla, D.R., and Jana, P.K. (November, January 30). A grid clustering algorithm using cluster boundaries. Proceedings of the 2012 World Congress on Information and Communication Technologies, Trivandrum, India.","DOI":"10.1109\/WICT.2012.6409084"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.datak.2013.05.002","article-title":"On detection of emerging anomalous traffic patterns using GPS data","volume":"87","author":"Pang","year":"2013","journal-title":"Data Knowl. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"and Saptawati, G.A.P. (2017, January 1\u20132). Spatio-temporal Mining to Identify Potential Traffic Congestion Based on Transportation Mode. Proceedings of the 2017 International Conference on Data and Software Engineering (ICoDSE), Palembang, Indonesia.","DOI":"10.1109\/ICODSE.2017.8285857"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.cor.2019.04.013","article-title":"The stratified p-center problem","volume":"108","year":"2019","journal-title":"Comput. Oper. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sun, X., Zhang, Y., Qin, G., Dong, H., and Guan, F. (2012, January 15\u201317). Pedestrian transfer time optimization of urban rail transit based on ACP approach. Proceedings of the 2012 IEEE International Conference on Automation and Logistics, Zhengzhou, China.","DOI":"10.1109\/ICAL.2012.6308176"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yao, L., Sun, L., and Wang, W. (2010, January 4\u20138). Subway Station Selection Model Based on Utility Combined Analysis. Proceedings of the Tenth International Conference of Chinese Transportation Professionals, Beijing, China.","DOI":"10.1061\/41127(382)6"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, Q., and Sun, H. (2018, January 3\u20135). A Decision Method for Subway Station Selection Based on the Optimal Closeness Coefficient. Proceedings of the 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE), Singapore.","DOI":"10.1109\/ICITE.2018.8492675"},{"key":"ref_28","first-page":"49","article-title":"Review and Thinking on Code for Transport Planning on Urban Road: Importance of Traffic Organization in Road Network Planning","volume":"41","author":"Huang","year":"2017","journal-title":"City Plan. Rev."},{"key":"ref_29","first-page":"38","article-title":"The Coverage Ratio of Bus Stations and an Evaluation of Spatial Patterns of Major Chinese Cities","volume":"226","author":"Li","year":"2015","journal-title":"Urban Plan. Forum"},{"key":"ref_30","unstructured":"Bishop, C. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, Z., Xiong, H., Gao, X., and Wu, J. (2010). Understanding of Internal Clustering Validation Measures, IEEE Computer Society.","DOI":"10.1109\/ICDM.2010.35"},{"key":"ref_33","first-page":"1","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1016\/j.tra.2012.02.003","article-title":"Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study","volume":"46","author":"Jenelius","year":"2012","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_35","first-page":"39","article-title":"Map Matching Algorithm of Large Scale Probe Vehicle Data","volume":"7","author":"Zhang","year":"1992","journal-title":"J. Transp. Syst. Eng. Inf. Technol."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/12\/804\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:37:50Z","timestamp":1760168270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/12\/804"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,30]]},"references-count":35,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["ijgi10120804"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10120804","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2021,11,30]]}}}