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Authors
Abstract(s)
O avanço tecnológico e o crescimento populacional dos últimos anos trouxe uma alta
demanda por soluções inteligentes que pudessem melhorar a qualidade de vida da população.
Uma dessas soluções é o Smart Parking (estacionamentos inteligentes). Esse
conceito integra diferentes áreas e tem por objetivo reduzir o fluxo de trânsito de cidades
por meio da implementação de sistemas inteligentes, focados no controle e gestão de estacionamentos.
O presente trabalho integrou o desenvolvimento de um modelo de Smart
Parking já estruturado, o qual foi concebido de forma gradual por alunos e professores da
UTFPR e IPB. Propôs-se a criação de uma estrutura de dados que integrasse todos os
módulos do sistema. Além disso, foi proposto um sistema que pudesse auxiliar na tomada
de decisões do produto, utilizando como base o grande volume de dados gerados por esse
tipo de aplicação. Com isso, no decorrer do trabalho é apresentado o modelo conceitual
utilizado na integração dos módulos, seguido de etapas de mineração e análise de dados.
Também é abordada a criação de um modelo para simulação de dados e a implementação
de algoritmos de machine learning (K-Means e Random Forest) e deep learning (LSTM)
focados na previsão de demanda de estacionamentos. A aplicação dos algoritmos mostrou
bons resultados na previsão de demanda, sendo os melhores obtidos pelo Random Forest.
Por fim, é apresentada uma ferramenta modular, que integrou processos de mineração e
análise de dados, fornecendo aos gestores um sistema para auxiliar na tomada de decisões
do produto.
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.
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.
Description
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do Paraná
Keywords
Smart parking Data mining Deep learning Business intelligence