Utilização de data mining e deep learning para business intelligence em estrutura integrada de sistema smart parking

Abstract

The technological advance and population growth of the last years brought a high demand for intelligent solutions that could improve the population’s quality of life. One of these solutions is Smart Parking. This concept integrates different areas and aims to reduce the traffic flow of cities through the implementation of intelligent systems, focused on the control and management of parking lots. The present work integrated the development of an already structured Smart Parking System, which was conceived gradually by students and professors from UTFPR and IPB. It was proposed the creation of a data structure that integrated all the system modules. Moreover, a system that could help in the decision making process of the product was proposed, using as base the large volume of data generated by this kind of application. Consequently, the conceptual model used in the integration of the modules is presented, followed by the data mining and analysis steps. The creation of a model for data simulation and the implementation of machine learning (K-Means and Random Forest) and deep learning (LSTM) algorithms, focused on parking lot demand forecasting are also addressed. The application of the algorithms showed good results in predicting demand, the best results being obtained by Random Forest. Finally, a modular tool is presented, which integrated data mining and analysis processes, providing managers with a system to assist in product decision making.