{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:46:59Z","timestamp":1760122019879,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T00:00:00Z","timestamp":1674950400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>In this paper, a measure is proposed that, based on the trajectories of moving objects, computes the speed limit\u00a0rate in each of the cells in which a region is segmented (the space where the objects move). The time is also segmented into intervals. In this way, the behavior of moving objects can be analyzed with regard to their speed in a cell for a given time interval. An implementation of the corresponding algorithm for this measure and several experiments were conducted with the trajectories of taxis in Porto (Portugal). The results showed that the speed limit\u00a0rate measure can be helpful for detecting patterns of movement, e.g., in a day (morning hours vs. night hours) or on different days of the week (weekdays vs. weekends). This measure might also serve as a rough estimate for congestion in a (sub)region. This may be useful for traffic analysis, including traffic prediction.<\/jats:p>","DOI":"10.3390\/informatics10010015","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:28:34Z","timestamp":1675045714000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Moving Objects Behavior Analysis: Region Speed Limit Rate Measure"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7806-6278","authenticated-orcid":false,"given":"Francisco Javier","family":"Moreno Arboleda","sequence":"first","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n y de la Decisi\u00f3n, Universidad Nacional de Colombia, Sede Medell\u00edn, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1892-4183","authenticated-orcid":false,"given":"Georgia","family":"Garani","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Thessaly, 41500 Larisa, Greece"}]},{"given":"Simon","family":"Zea Gallego","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias de la Computaci\u00f3n y de la Decisi\u00f3n, Universidad Nacional de Colombia, Sede Medell\u00edn, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1080\/15230406.2014.890071","article-title":"How to Compare Movement? A Review of Physical Movement Similarity Measures in Geographic Information Science and Beyond","volume":"41","author":"Ranacher","year":"2014","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.eij.2016.07.001","article-title":"A Generic Trajectory Similarity Operator in Moving Object Databases","volume":"18","author":"Magdy","year":"2017","journal-title":"Egypt. Inform. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1057\/PALGRAVE.IVS.9500182","article-title":"Towards a Taxonomy of Movement Patterns","volume":"7","author":"Dodge","year":"2008","journal-title":"Inf. Vis."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yanagisawa, Y., and Satoh, T. (2006, January 3\u20137). Clustering Multidimensional Trajectories Based on Shape and Velocity. 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Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"286","DOI":"10.7708\/ijtte.2012.2(4).01","article-title":"Measuring Urban Traffic Congestion\u2014A Review","volume":"2","author":"Rao","year":"2012","journal-title":"Int. J. Traffic Transp. Eng."},{"key":"ref_9","first-page":"1","article-title":"Discovering Traffic Congestion through Traffic Flow Patterns Generated by Moving Object Trajectories","volume":"80","author":"Kohan","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/jdwm.2011010101","article-title":"Visual Mobility Analysis Using T-Warehouse","volume":"7","author":"Leonardi","year":"2011","journal-title":"Int. J. Data Warehous. Min."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s41324-019-00269-x","article-title":"A Survey on Trajectory Data Warehouse","volume":"28","author":"Alsahfi","year":"2020","journal-title":"Spat. Inf. Res."},{"key":"ref_12","unstructured":"Malinowski, E., and Zim\u00e1nyi, E. (2008). Advanced Data Warehouse Design, Springer."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s10707-013-0181-3","article-title":"A General Framework for Trajectory Data Warehousing and Visual OLAP","volume":"18","author":"Leonardi","year":"2014","journal-title":"GeoInformatica"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.asoc.2017.07.014","article-title":"Measuring Traffic Congestion: An Approach Based on Learning Weighted Inequality, Spread and Aggregation Indices from Comparison Data","volume":"67","author":"Beliakov","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"de Almeida, D., de Souza Baptista, C., de Andrade, F., and Soares, A. (2020). A Survey on Big Data for Trajectory Analytics. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020088"},{"key":"ref_16","first-page":"1","article-title":"A Survey on Trajectory Data Management, Analytics, and Learning","volume":"54","author":"Wang","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_17","unstructured":"U.S (2023, January 06). Department of Transportation, Federal Highway Administration. Traffic Analysis Tools Program, Available online: https:\/\/ops.fhwa.dot.gov\/trafficanalysistools\/type_tools.htm."},{"key":"ref_18","unstructured":"Traffic Micro-simulation model (2023, January 06). Multicriteria Planning (Mcrit Ltd). Available online: https:\/\/mcrit.com\/services\/systems-and-software-development\/traffic-micro-simulation-models."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Afrin, T., and Yodo, N. (2020). A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System. Sustainability, 12.","DOI":"10.3390\/su12114660"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s41019-020-00151-z","article-title":"A Survey of Traffic Prediction: From Spatio-Temporal Data to Intelligent Transportation","volume":"6","author":"Yuan","year":"2021","journal-title":"Data Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, L., and \u00d6zsu, M.T. (2009). Encyclopedia of Database Systems, Springer.","DOI":"10.1007\/978-0-387-39940-9"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1145\/182.358434","article-title":"Maintaining Knowledge about Temporal Intervals","volume":"26","author":"Allen","year":"1983","journal-title":"Commun. ACM"},{"key":"ref_23","unstructured":"Dua, D., and Graff, C. (2019). UCI Machine Learning Repository, School of Information and Computer Science, University of California. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets\/Taxi+Service+Trajectory+-+Prediction+Challenge,+ECML+PKDD+2015."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.proeng.2016.01.277","article-title":"A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index","volume":"137","author":"He","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","article-title":"Short-Term Traffic Forecasting: Where We are and Where We\u2019re Going","volume":"43","author":"Vlahogianni","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"130","DOI":"10.3846\/16484142.2004.9637965","article-title":"Vehicle Speed Control Using Road Bumps","volume":"19","author":"Salau","year":"2004","journal-title":"Transport"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.eij.2011.02.007","article-title":"An Extended K-Means Technique for Clustering Moving Objects","volume":"12","author":"Ossama","year":"2011","journal-title":"Egypt. Inform. 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