{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:32:57Z","timestamp":1761863577031,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2016,5,18]],"date-time":"2016-05-18T00:00:00Z","timestamp":1463529600000},"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>Traffic congestion clustering judgment is a fundamental problem in the study of traffic jam warning. However, it is not satisfactory to judge traffic congestion degrees using only vehicle speed. In this paper, we collect traffic flow information with three properties (traffic flow velocity, traffic flow density and traffic volume) of urban trunk roads, which is used to judge the traffic congestion degree. We first define a grey relational clustering model by leveraging grey relational analysis and rough set theory to mine relationships of multidimensional-attribute information. Then, we propose a grey relational membership degree rank clustering algorithm (GMRC) to discriminant clustering priority and further analyze the urban traffic congestion degree. Our experimental results show that the average accuracy of the GMRC algorithm is 24.9% greater than that of the K-means algorithm and 30.8% greater than that of the Fuzzy C-Means (FCM) algorithm. Furthermore, we find that our method can be more conducive to dynamic traffic warnings.<\/jats:p>","DOI":"10.3390\/ijgi5050071","type":"journal-article","created":{"date-parts":[[2016,5,18]],"date-time":"2016-05-18T10:10:24Z","timestamp":1463566224000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis"],"prefix":"10.3390","volume":"5","author":[{"given":"Yingya","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer, Nanjing University of Post and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Computer, Nanjing University of Post and Telecommunications, Nanjing 210003, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruchuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education, Nanjing University of Post and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2763-8085","authenticated-orcid":false,"given":"Reza","family":"Malekian","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Inform\u00e1tica, Universidad de Santiago de Chile, Av. Ecuador, Santiago 3659, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,5,18]]},"reference":[{"key":"ref_1","unstructured":"Courtney, R.L. (1997, January 9\u201312). A broad view of its standards in the U.S.. Proceedings of the IEEE Conference on Intelligent Transportation Systems, Boston, MA, USA."},{"key":"ref_2","unstructured":"Arnold, E.D. Congestion on Virginia\u2019s Urban Highways, Available online: http:\/\/ntl.bts.gov\/DOCS\/arnold.html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1016\/j.patcog.2012.02.003","article-title":"A robust adaptive clustering analysis method for automatic identification of clusters","volume":"45","author":"Mok","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_4","first-page":"108","article-title":"New grey comprehensive correlation degree model and its application","volume":"32","author":"Yu","year":"2013","journal-title":"Technol. 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Syst."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/5\/71\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:24:05Z","timestamp":1760210645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/5\/71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,5,18]]},"references-count":15,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2016,5]]}},"alternative-id":["ijgi5050071"],"URL":"https:\/\/doi.org\/10.3390\/ijgi5050071","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2016,5,18]]}}}