Estimating Parking Lot Occupancy Based on Traffic Congestion for Route Planning

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dc.contributor.authorAdar, Uğur Güven
dc.contributor.authorÇayli, Osman
dc.contributor.authorYilmaz, Atınç
dc.date.accessioned2025-03-09T10:57:47Z
dc.date.available2025-03-09T10:57:47Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description1st International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023 -- 16 November 2023 through 17 November 2023 -- Istanbul -- 326219
dc.description.abstractTraffic congestion in large cities negatively affects daily life and is a significant concern. In crowded cities like Istanbul, not only waiting in traffic but also the availability of parking spaces at the destination is a critical issue. Predicting the occupancy of parking lots in advance can supply a significant advantage for drivers. In this study, traffic congestion in Istanbul is attempted to be estimated using artificial intelligence techniques such as K-NN, Decision Trees, and Artificial Neural Networks, with 2022 vehicle traffic data from the Istanbul Metropolitan Municipality (IBB) Open Data Portal. As a result of the tests, the decision tree method provided the best results in the dataset, estimating traffic congestion with an R2 value of 0.8469. The occupancy status of parking lots in areas close to the traffic congestion estimation points was determined using the İspark Parking Detailed Information dataset. Based on the obtained information, it is aimed to predict the occupancy status of parking lots according to traffic congestion without the need for live data using the Dijkstra algorithm in the future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
dc.identifier.doi10.1007/978-3-031-81455-6_2
dc.identifier.endpage31
dc.identifier.isbn978-303181454-9
dc.identifier.issn1865-0929
dc.identifier.scopus2-s2.0-85218506287
dc.identifier.scopusqualityQ3
dc.identifier.startpage19
dc.identifier.urihttps://doi.org/10.1007/978-3-031-81455-6_2
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4988
dc.identifier.volume2204
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250310
dc.subjectartificial neural networks
dc.subjectdecision trees
dc.subjectk-nn
dc.subjectparking lot
dc.subjecttraffic congestion estimation
dc.titleEstimating Parking Lot Occupancy Based on Traffic Congestion for Route Planning
dc.typeConference Object

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