Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction
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)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/13/12/449 |
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| author | Hasan Kemik Tugba Dalyan Murat Aydogan |
| author_facet | Hasan Kemik Tugba Dalyan Murat Aydogan |
| author_sort | Hasan Kemik |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ab4b7a32543e4b27b3ef3c7a1048e28c |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-ab4b7a32543e4b27b3ef3c7a1048e28c2025-08-20T02:00:38ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-12-01131244910.3390/ijgi13120449Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking PredictionHasan Kemik0Tugba Dalyan1Murat Aydogan2Department of Computer Engineering, İstanbul Bilgi University, 34050 Istanbul, TurkeyDepartment of Computer Engineering, İstanbul Bilgi University, 34050 Istanbul, TurkeyDepartment of Software Engineering, Firat University, 23100 Elazığ, TurkeyFinding 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.https://www.mdpi.com/2220-9964/13/12/449smart citysmart parkingdeep learningLSTMMHA |
| spellingShingle | Hasan Kemik Tugba Dalyan Murat Aydogan Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction ISPRS International Journal of Geo-Information smart city smart parking deep learning LSTM MHA |
| title | Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction |
| title_full | Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction |
| title_fullStr | Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction |
| title_full_unstemmed | Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction |
| title_short | Smart Solutions for Mega-Cities: Utilizing Long Short-Term Memory and Multi-Head Attention in Parking Prediction |
| title_sort | smart solutions for mega cities utilizing long short term memory and multi head attention in parking prediction |
| topic | smart city smart parking deep learning LSTM MHA |
| url | https://www.mdpi.com/2220-9964/13/12/449 |
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