{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T20:17:57Z","timestamp":1767903477394,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["02K15A040"],"award-info":[{"award-number":["02K15A040"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["03SF0547"],"award-info":[{"award-number":["03SF0547"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006360","name":"Bundesministerium f\u00fcr Wirtschaft und Energie","doi-asserted-by":"publisher","award":["01MD19007B"],"award-info":[{"award-number":["01MD19007B"]}],"id":[{"id":"10.13039\/501100006360","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006360","name":"Bundesministerium f\u00fcr Wirtschaft und Energie","doi-asserted-by":"publisher","award":["01ME19009B"],"award-info":[{"award-number":["01ME19009B"]}],"id":[{"id":"10.13039\/501100006360","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.<\/jats:p>","DOI":"10.3390\/ijgi10040248","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T11:58:45Z","timestamp":1617883125000},"page":"248","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Mining Topological Dependencies of Recurrent Congestion in Road Networks"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0911-6264","authenticated-orcid":false,"given":"Nicolas","family":"Tempelmeier","sequence":"first","affiliation":[{"name":"L3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0781-5395","authenticated-orcid":false,"given":"Udo","family":"Feuerhake","sequence":"additional","affiliation":[{"name":"Institute of Cartography and Geoinformatics, Leibniz University Hannover, 30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oskar","family":"Wage","sequence":"additional","affiliation":[{"name":"Institute of Cartography and Geoinformatics, Leibniz University Hannover, 30167 Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5134-9072","authenticated-orcid":false,"given":"Elena","family":"Demidova","sequence":"additional","affiliation":[{"name":"Data Science &amp; Intelligent Systems Group (DSIS), University of Bonn, D-53012 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1140\/epjds\/s13688-019-0207-7","article-title":"Identifying the most influential roads based on traffic correlation networks","volume":"8","author":"Guo","year":"2019","journal-title":"EPJ Data Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3141\/1867-08","article-title":"Methodology for Measuring Recurrent and Nonrecurrent Traffic Congestion","volume":"1867","author":"Dowling","year":"2004","journal-title":"Transp. 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