Deep Learning-Based Mobile Application Design for Smart Parking

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dc.authoridCANLI, Hikmet/0000-0003-3394-7113
dc.contributor.authorCanli, H.
dc.contributor.authorToklu, S.
dc.date.accessioned2021-12-01T18:48:13Z
dc.date.available2021-12-01T18:48:13Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractIn the era of Internet of Things (IoT) and smart city ecosystems, there is a need for innovative smart parking systems for more sustainable cities. With the increasing number of vehicles in the cities every year, it takes more time to find parking spaces. The solution methods developed are no longer sufficient. The time that passes while waiting for a parking space in traffic carries with it problems such as energy, environmental pollution and stress. In this study, a deep learning and cloud-based new mobile smart parking application was developed to minimize the problem of searching for parking spaces. Within the application, a service has been developed based on deep learning with Long short-term memory (LSTM) to predict the parking space. Here, dynamic access is provided to the LSTM-based model previously created through the mobile device of the user, and the process of displaying the occupancy rates of the parks at the desired place is accomplished on the mobile device by entering the relevant parameters. By this means, both energy and time savings have been achieved. With the real-time car parking data collected in the city of Istanbul in Turkey, high accuracy results were obtained. In order to demonstrate the effectiveness of the model proposed, it was compared with the Support Vector Machine, Random Forest and ARIMA methods. The results have confirmed the high accuracy and reliability that was promised.en_US
dc.identifier.doi10.1109/ACCESS.2021.3074887
dc.identifier.endpage61183en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85104592406en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage61171en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2021.3074887
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10484
dc.identifier.volume9en_US
dc.identifier.wosWOS:000645071600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectAutomobilesen_US
dc.subjectSupport vector machinesen_US
dc.subjectVehiclesen_US
dc.subjectSmart citiesen_US
dc.subjectSpace vehiclesen_US
dc.subjectInternet of Thingsen_US
dc.subjectSmart cityen_US
dc.subjectdeep learningen_US
dc.subjectLSTMen_US
dc.subjectsupport vector machineen_US
dc.subjectrandom foresten_US
dc.subjectARIMAen_US
dc.titleDeep Learning-Based Mobile Application Design for Smart Parkingen_US
dc.typeArticleen_US

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