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Smart city article details

Title	Smart Solutions For Mega-Cities: Utilizing Long Short-Term Memory And Multi-Head Attention In Parking Prediction
ID_Doc	51430
Authors	Kemik H.; Dalyan T.; Aydogan M.
Year	2024
Published	ISPRS International Journal of Geo-Information, 13, 12
DOI	http://dx.doi.org/10.3390/ijgi13120449
Abstract	Finding 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. © 2024 by the authors.
Author Keywords	deep learning; LSTM; MHA; smart city; smart parking

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