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Title	Data Mining Models To Predict Parking Lot Availability
ID_Doc	17277
Authors	Rodrigues B.; Fernandes C.; Vieira J.; Portela F.
Year	2023
Published	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14116 LNAI
DOI	http://dx.doi.org/10.1007/978-3-031-49011-8_42
Abstract	With the growth of IoT (Internet of Things) technologies, there has been a significant increase in opportunities to enhance various aspects of our daily lives. One such application is the prediction of car park occupancy using car park movement data, which can be further improved by incorporating weather data. This paper focuses on investigating how weather conditions influence car park occupancy prediction and aims to identify the most effective prediction algorithm. To achieve more accurate results, the researchers explored two primary approaches: Classification and Regression. These approaches allow for a comprehensive analysis of the parking scenario, catering to both qualitative and quantitative aspects of predicting car park occupancy. In this study, a total of 24 prediction models, encompassing a wide range of algorithms were induced. These models were designed to consider various details, including parking features, location specifics, time-related factors and crucially, weather conditions. Overall, this study showcased the potential of leveraging IoT technologies, car park movement data, and weather information to predict car park occupancy effectively. By exploring both classification and regression approaches, each yielding accuracy and R2Score values surpassing 85%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Data Mining; Parking Lot; Smart Cities

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