{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T19:54:48Z","timestamp":1771012488357,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2013,5,10]],"date-time":"2013-05-10T00:00:00Z","timestamp":1368144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In this paper, we explore spatio-temporal clusters using massive floating car data from a complex network perspective. We analyzed over 85 million taxicab GPS points (floating car data) collected in Wuhan, Hubei, China. Low-speed and stop points were selected to generate spatio-temporal clusters, which indicated the typical stop-and-go movement pattern in real-world traffic congestion. We found that the sizes of  spatio-temporal clusters exhibited a power law distribution. This implies the presence of a scaling property; i.e., they can be naturally divided into a strong hierarchical structure: long time-duration ones (a low percentage) whose values lie above the mean value and short ones (a high percentage) whose values lie below. The spatio-temporal clusters at different levels represented the degree of traffic congestions, for example the higher the level, the worse the traffic congestions. Moreover, the distribution of traffic congestions varied spatio-temporally and demonstrated a multinuclear structure in urban road networks,  which suggested there is a correlation to the corresponding internal mobile regularities of an urban system.<\/jats:p>","DOI":"10.3390\/ijgi2020371","type":"journal-article","created":{"date-parts":[[2013,5,10]],"date-time":"2013-05-10T14:17:28Z","timestamp":1368195448000},"page":"371-384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data"],"prefix":"10.3390","volume":"2","author":[{"given":"Xintao","family":"Liu","sequence":"first","affiliation":[{"name":"Division of Geoinformatics, Royal Institute of Technology (KTH), Stockholm SE-10044, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1369-3216","authenticated-orcid":false,"given":"Yifang","family":"Ban","sequence":"additional","affiliation":[{"name":"Division of Geoinformatics, Royal Institute of Technology (KTH), Stockholm SE-10044, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2013,5,10]]},"reference":[{"key":"ref_1","unstructured":"Dhingra, S.L., and Gull, I. 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