{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T21:05:01Z","timestamp":1775941501430,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["KK.01.1.1.01.0009 (DATACROSS)"],"award-info":[{"award-number":["KK.01.1.1.01.0009 (DATACROSS)"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other models regarding simultaneously capturing spatial and temporal components, which are of immense importance in traffic dataset analysis. This paper presents a tensor-based method for extracting the spatiotemporal road traffic patterns represented with the speed transition matrices, with the goal of anomaly detection. A novel anomaly detection approach is presented, which relies on computing the center of mass of the observed traffic patterns. The method was evaluated on a large road traffic dataset and was able to detect the most anomalous parts of the urban road network. By analyzing spatial and temporal components of the most anomalous traffic patterns, sources of anomalies can be identified. Results were validated using the extracted domain knowledge from the Highway Capacity Manual. The anomaly detection model achieved a precision score of 92.88%. Therefore, this method finds its usages for safety experts in detecting potentially dangerous road segments, urban traffic planners, and routing applications.<\/jats:p>","DOI":"10.3390\/app112412017","type":"journal-article","created":{"date-parts":[[2021,12,19]],"date-time":"2021-12-19T20:41:55Z","timestamp":1639946515000},"page":"12017","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8257-8356","authenticated-orcid":false,"given":"Leo","family":"Ti\u0161ljari\u0107","sequence":"first","affiliation":[{"name":"Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-7155","authenticated-orcid":false,"given":"Sofia","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Laboratory of Artificial Intelligence and Decision Support, Institute for Systems and Computer Engineering, Technology and Science, University of Porto, 4200 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8564-4304","authenticated-orcid":false,"given":"Ton\u010di","family":"Cari\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3357-1195","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Gama","sequence":"additional","affiliation":[{"name":"Laboratory of Artificial Intelligence and Decision Support, Institute for Systems and Computer Engineering, Technology and Science, University of Porto, 4200 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1109\/TITS.2020.3030707","article-title":"Multi-Modal Combined Route Choice Modeling in the MaaS Age Considering Generalized Path Overlapping Problem","volume":"22","author":"Li","year":"2021","journal-title":"IEEE Trans. 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