Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction

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dc.authorid0000-0002-6876-6454
dc.authorid0000-0002-5868-5407
dc.contributor.authorKemik, Hasan
dc.contributor.authorDalyan, Tugba
dc.contributor.authorAydogan, Murat
dc.date.accessioned2026-04-04T18:56:07Z
dc.date.available2026-04-04T18:56:07Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractFinding a parking space is a major concern in megacities, causing drivers to lose time and money while contributing to atmospheric pollution and global warming. This study proposes a method to predict parking slot availability by comparing Long Short-Term Memory (LSTM) and Multi-Head Attention (MHA) methods using the CityPulse Smart City Datasets. The initial experiments assessed the impact of pollution and time features on prediction accuracy. In a subsequent experiment, the dataset was expanded by incorporating weather-related features and a broader time range while excluding pollution and time features, as informed by the initial results. Various experiments were conducted with different parameters, such as model depth and activation functions. The results demonstrated that MHA outperformed LSTM in predicting occupancy rates, achieving a Mean Absolute Error (MAE) score of 0.0589 on the extended dataset. This study marks a pioneering effort in using MHA for real-time parking occupancy prediction, showcasing significant success with fewer parameters and a smaller model size.
dc.description.sponsorshipIdot;stanbul Bilgi University, Research Development and Innovation (RDI) Fund
dc.description.sponsorshipThis research was supported by the Istanbul Bilgi University, Research Development and Innovation (RDI) Fund, through the project titled 'Towards Sustainable Cities: Smart Parking System and Policy Development in Istanbul'. The authors gratefully acknowledge this support, which has made this academic contribution possible.
dc.identifier.doi10.3390/ijgi13120449
dc.identifier.doi10.3390/ijgi13120449
dc.identifier.issn2220-9964
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85213292182
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/ijgi13120449
dc.identifier.urihttps://hdl.handle.net/11411/10700
dc.identifier.volume13
dc.identifier.wosWOS:001384748200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofIsprs International Journal of Geo-Information
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectSmart City
dc.subjectSmart Parking
dc.subjectDeep Learning
dc.subjectLstm
dc.subjectMha
dc.titleSmart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction
dc.typeArticle

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