A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/1/322 |
| _version_ | 1798042441168715776 |
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| author | Faraz Malik Awan Yasir Saleem Roberto Minerva Noel Crespi |
| author_facet | Faraz Malik Awan Yasir Saleem Roberto Minerva Noel Crespi |
| author_sort | Faraz Malik Awan |
| collection | DOAJ |
| description | Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots. |
| first_indexed | 2024-04-11T22:35:34Z |
| format | Article |
| id | doaj.art-1ae1b653fcb24af6b804502d50311382 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:35:34Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1ae1b653fcb24af6b804502d503113822022-12-22T03:59:14ZengMDPI AGSensors1424-82202020-01-0120132210.3390/s20010322s20010322A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability PredictionFaraz Malik Awan0Yasir Saleem1Roberto Minerva2Noel Crespi3CNRS UMR5157, Telecom SudParis, Institut Polytechnique de Paris, 91000 Evry, FranceCNRS UMR5157, Telecom SudParis, Institut Polytechnique de Paris, 91000 Evry, FranceCNRS UMR5157, Telecom SudParis, Institut Polytechnique de Paris, 91000 Evry, FranceCNRS UMR5157, Telecom SudParis, Institut Polytechnique de Paris, 91000 Evry, FranceMachine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.https://www.mdpi.com/1424-8220/20/1/322car parkingdecision treedeep learningensemble learningiotk-nearest neighbors (knn)machine learningmultilayer perceptronparking sensorsrandom forestsensorssmart cityvoting classifier |
| spellingShingle | Faraz Malik Awan Yasir Saleem Roberto Minerva Noel Crespi A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction Sensors car parking decision tree deep learning ensemble learning iot k-nearest neighbors (knn) machine learning multilayer perceptron parking sensors random forest sensors smart city voting classifier |
| title | A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction |
| title_full | A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction |
| title_fullStr | A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction |
| title_full_unstemmed | A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction |
| title_short | A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction |
| title_sort | comparative analysis of machine deep learning models for parking space availability prediction |
| topic | car parking decision tree deep learning ensemble learning iot k-nearest neighbors (knn) machine learning multilayer perceptron parking sensors random forest sensors smart city voting classifier |
| url | https://www.mdpi.com/1424-8220/20/1/322 |
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