Time-persistent regions discovery of taxi trajectory big datasets based on regional spatio-temporal velocity

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dc.authoridDokuz, Ahmet Sakir/0000-0002-1775-0954
dc.contributor.authorDokuz, Yesim
dc.contributor.authorDokuz, Ahmet Sakir
dc.date.accessioned2024-11-07T13:24:58Z
dc.date.available2024-11-07T13:24:58Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractWith the increasing number of residents and motor vehicles in urban areas, traffic-related problems have emerged. Traffic analysis and prediction systems provide in-formation about the city dynamics and traffic estimation on a regional basis. Several studies are performed for traffic analysis and prediction in urban datasets, including taxi trajectory datasets, however, these studies do not focus on regional traffic analysis and time-persistent regions discovery. Time-persistent regions refer to the regions that have a stable utilization and relatively stationary velocity in terms of traffic activities. In this study, a novel method is proposed to discover time-persistent regions based on regional daily velocity values using taxi trajectory big datasets. A new algorithm, namely Time-Persistent Regions Discovery algorithm (TPRD algorithm), is proposed based on the proposed method. The proposed TPRD algorithm is experimentally evaluated on TLC Taxi Trip Records big dataset of New York City and the results show that the proposed algorithm could discover time-persistent regions based on proposed interest measures and threshold values. & COPY; 2023 Elsevier B.V. All rights reserved.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Nigde Omer Halisdemir University [MMT 2021/12-BAGEP]
dc.description.sponsorshipThis research has been supported by the Scientific Research Projects Coordination Unit of Nigde Omer Halisdemir University, Project Number: MMT 2021/12-BAGEP.
dc.identifier.doi10.1016/j.physa.2023.128843
dc.identifier.issn0378-4371
dc.identifier.issn1873-2119
dc.identifier.scopus2-s2.0-85159609351
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.physa.2023.128843
dc.identifier.urihttps://hdl.handle.net/11480/14434
dc.identifier.volume623
dc.identifier.wosWOS:001055338200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofPhysica A-Statistical Mechanics and Its Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectTaxi trajectory mining
dc.subjectUrban computing
dc.subjectBig data analytics
dc.subjectTime-persistent regions discovery
dc.titleTime-persistent regions discovery of taxi trajectory big datasets based on regional spatio-temporal velocity
dc.typeArticle

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