Sensors, Vol. 20, Pages 322: A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Sensors, Vol. 20, Pages 322: A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction Sensors doi: 10.3390/s20010322 Authors: Faraz Malik Awan Yasir Saleem Roberto Minerva Noel Crespi 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 o...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research