Anomaly Detection in Region Mobility Utilization Using Daily Taxi Trajectory Dataset

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dc.authoridDokuz, Ahmet Sakir/0000-0002-1775-0954
dc.authoridDokuz, Yesim/0000-0001-7202-2899
dc.contributor.authorDokuz, Yesim
dc.contributor.authorDokuz, Ahmet Sakir
dc.date.accessioned2024-11-07T13:24:03Z
dc.date.available2024-11-07T13:24:03Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description6th International Conference on Smart City Applications -- OCT 27-29, 2021 -- Safranbolu, TURKEY
dc.description.abstractAnomaly detection in urban big datasets is getting wide attention with the presence of different and various urban big data sources. Urban anomaly detection is an important application area because discovered anomalies in urban areas would provide essential information about urban areas and their utilization, especially human mobility analytics and traffic condition monitoring. In the literature, there are several studies performed for urban anomaly detection using taxi trajectory datasets, such as events detection, regional urban anomaly detection and traffic incident detection. In this study, anomaly detection in regional mobility utilization of daily taxi trajectory datasets is performed based on DBSCAN clustering algorithm. A new algorithm and a threshold value are proposed to detect taxi regions as normal and anomalous for both incoming and outgoing taxi trip records. Experiments are performed on New York taxi trajectory big dataset and the experimental results show that proposed algorithm is efficient on detecting regions as normal or anomalous based on daily taxi trip record counts.
dc.identifier.doi10.1007/978-3-030-94191-8_19
dc.identifier.endpage247
dc.identifier.isbn978-3-030-94191-8
dc.identifier.isbn978-3-030-94190-1
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85126374862
dc.identifier.scopusqualityQ4
dc.identifier.startpage237
dc.identifier.urihttps://doi.org/10.1007/978-3-030-94191-8_19
dc.identifier.urihttps://hdl.handle.net/11480/13862
dc.identifier.volume393
dc.identifier.wosWOS:000928840400019
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.relation.ispartof6th International Conference on Smart City Applications
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectUrban anomaly detection
dc.subjectRegion mobility utilization
dc.subjectTaxi trajectory big data mining
dc.titleAnomaly Detection in Region Mobility Utilization Using Daily Taxi Trajectory Dataset
dc.typeConference Object

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