Traffic 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.


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    Titel :

    Estimating Parking Lot Occupancy Based on Traffic Congestion for Route Planning


    Weitere Titelangaben:

    Communic.Comp.Inf.Science


    Beteiligte:
    Mirzazadeh, A. (Herausgeber:in) / Molamohamadi, Zohreh (Herausgeber:in) / Babaee Tirkolaee, Efran (Herausgeber:in) / Weber, Gerhard-Wilhelm (Herausgeber:in) / Leung, Janny (Herausgeber:in) / Adar, Uğur Güven (Autor:in) / Çayli, Osman (Autor:in) / Yilmaz, Atınç (Autor:in)

    Kongress:

    International Conference on Optimization and Data Science in Industrial Engineering ; 2023 ; Istanbul, Türkiye November 16, 2023 - November 17, 2023



    Erscheinungsdatum :

    26.01.2025


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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