Uma Abordagem para Geração de Séries Temporais de Mobilidade Urbana Baseada em Aprendizado Profundo

• Iran F. Ribeiro UFES
• Gabriel Simoura UFES
• Heitor S. Ramos UFMG
• Giovanni Comarela UFES
• Vinícius F. S. Mota UFES

DOI: https://doi.org/10.5753/courb.2021.17118

Abstract

One of the major challenges in the gathering and dissemination of urban mobility data remains on the fact that it contains information that can compromise users' privacy. An alternative to tackle this problem is the generation of synthetic datasets that may preserve the characteristics of the real data. This work evaluates such synthetic generation of time series based on urban mobility by using a classical statistical model and deep learning algorithms, such as Generative Adversarial Networks (GANs). We compare these time series against the original data by visual and quantitative analysis. Results showed that the models based on deep learning generate time series data with the same characteristics as the original dataset.

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