Improving parking availability prediction in smart cities with IoT and ensemble-based model
Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In additio...
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| Format: | Article |
| Language: | English |
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Springer
2022-03-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157819312613 |
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| author | Stéphane Cédric Koumetio Tekouabou El Arbi Abdellaoui Alaoui Walid Cherif Hassan Silkan |
| author_facet | Stéphane Cédric Koumetio Tekouabou El Arbi Abdellaoui Alaoui Walid Cherif Hassan Silkan |
| author_sort | Stéphane Cédric Koumetio Tekouabou |
| collection | DOAJ |
| description | Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity. |
| format | Article |
| id | doaj-art-5f12ead32bc9435f85d2cda16d79c8c2 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-5f12ead32bc9435f85d2cda16d79c8c22025-08-20T03:52:03ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-03-0134368769710.1016/j.jksuci.2020.01.008Improving parking availability prediction in smart cities with IoT and ensemble-based modelStéphane Cédric Koumetio Tekouabou0El Arbi Abdellaoui Alaoui1Walid Cherif2Hassan Silkan3Department of Computer Science, Laboratory LAROSERI, Faculty of Sciences, El Jadida, MoroccoEIGSI, 282 Route of the Oasis, Mâarif, 20140 Casablanca, Morocco; E3MI Research Team, Department of Computer Science, Faculty of Sciences and Techniques at Errachidia, University of Moulay Ismaïl, Route Meknes, 52000 Errachidia, Morocco; Corresponding author at: EIGSI, 282 Route of the Oasis, Mâarif, 20140 Casablanca, Morocco.Laboratory SI2M, National Institute of Statistics and Applied Economics, Rabat, MoroccoDepartment of Computer Science, Laboratory LAROSERI, Faculty of Sciences, El Jadida, MoroccoSmart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity.http://www.sciencedirect.com/science/article/pii/S1319157819312613Smart citiesParking availabilityIoTRegressionEnsemble models |
| spellingShingle | Stéphane Cédric Koumetio Tekouabou El Arbi Abdellaoui Alaoui Walid Cherif Hassan Silkan Improving parking availability prediction in smart cities with IoT and ensemble-based model Journal of King Saud University: Computer and Information Sciences Smart cities Parking availability IoT Regression Ensemble models |
| title | Improving parking availability prediction in smart cities with IoT and ensemble-based model |
| title_full | Improving parking availability prediction in smart cities with IoT and ensemble-based model |
| title_fullStr | Improving parking availability prediction in smart cities with IoT and ensemble-based model |
| title_full_unstemmed | Improving parking availability prediction in smart cities with IoT and ensemble-based model |
| title_short | Improving parking availability prediction in smart cities with IoT and ensemble-based model |
| title_sort | improving parking availability prediction in smart cities with iot and ensemble based model |
| topic | Smart cities Parking availability IoT Regression Ensemble models |
| url | http://www.sciencedirect.com/science/article/pii/S1319157819312613 |
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