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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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Autores principales: Awan, Faraz Malik, Saleem, Yasir, Minerva, Roberto, Crespi, Noel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6983166/
https://www.ncbi.nlm.nih.gov/pubmed/31935953
http://dx.doi.org/10.3390/s20010322
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author Awan, Faraz Malik
Saleem, Yasir
Minerva, Roberto
Crespi, Noel
author_facet Awan, Faraz Malik
Saleem, Yasir
Minerva, Roberto
Crespi, Noel
author_sort Awan, Faraz Malik
collection PubMed
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.
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spelling pubmed-69831662020-02-06 A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction Awan, Faraz Malik Saleem, Yasir Minerva, Roberto Crespi, Noel Sensors (Basel) Article 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. MDPI 2020-01-06 /pmc/articles/PMC6983166/ /pubmed/31935953 http://dx.doi.org/10.3390/s20010322 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Awan, Faraz Malik
Saleem, Yasir
Minerva, Roberto
Crespi, Noel
A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6983166/
https://www.ncbi.nlm.nih.gov/pubmed/31935953
http://dx.doi.org/10.3390/s20010322
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