Weighted spatio-temporal taxi trajectory big data mining for regional traffic estimation

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dc.authoridDokuz, Ahmet Sakir/0000-0002-1775-0954
dc.contributor.authorDokuz, Ahmet Sakir
dc.date.accessioned2024-11-07T13:24:56Z
dc.date.available2024-11-07T13:24:56Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe estimation of traffic conditions in cities is becoming essential to establish a sustainable transportation system and to help traffic management authorities plan the traffic of cities. Recently, taxi trajectory big datasets are being collected during taxi drivers are routing around the cities. Taxi trajectory datasets provide behavioral information about the city residents, urban flows of the taxi passengers, and infrastructure for traffic condition estimation. This study aims to estimate regional traffic velocity of New York City using New York taxi trajectory dataset. A new method is proposed that uses weighted spatio-temporal trajectory big data mining approach and scores each region of the cities in terms of traffic velocity. A new algorithm is proposed, namely Regional Traffic Velocity Estimation (RTVE) algorithm, which uses proposed regional spatio-temporal velocity estimation method and experimentally evaluated using New York taxi trajectory dataset. Experimental results show that each region in New York have different velocity and usage characteristics in terms of hourly and daily analyses. Also, borough-level analyses are performed that reveal knowledge about the boroughs of New York. The estimated regional traffic velocity of cities based on taxi trajectory datasets would provide a decision support system for decision-makers in terms of regional hourly and daily evaluation of cities with cost-free and widespread city traffic dataset. (C) 2021 Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.physa.2021.126645
dc.identifier.issn0378-4371
dc.identifier.issn1873-2119
dc.identifier.scopus2-s2.0-85120491109
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.physa.2021.126645
dc.identifier.urihttps://hdl.handle.net/11480/14415
dc.identifier.volume589
dc.identifier.wosWOS:000791272100008
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.subjectRegional traffic velocity estimation
dc.subjectRegional traffic condition monitoring
dc.subjectWeighted spatio-temporal pattern mining
dc.subjectBig data mining
dc.subjectTaxi trajectory dataset
dc.titleWeighted spatio-temporal taxi trajectory big data mining for regional traffic estimation
dc.typeArticle

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