Name: Iran Freitas Ribeiro
Type: MSc dissertation
Publication date: 03/09/2021
Advisor:

Namesort descending Role
Vinicius Fernandes Soares Mota Advisor *

Examining board:

Namesort descending Role
Antonio Augusto de Aragão Rocha External Examiner *
Rodolfo da Silva Villaca Internal Examiner *
Vinicius Fernandes Soares Mota Advisor *

Summary: Understanding the mobility among mobile network devices is critical for different types of
networks, such as wireless networks, vehicular networks, or ad hoc networks. In this sense,
the availability of urban mobility data is essential to evaluate these networks’ performance.
One can investigate the impact of mobility using synthetic models or realistic mobility data.
Although synthetic models attempt to reproduce real mobility characteristics, they may not
reflect the realism of the studied scenario. Furthermore, the gathering and disseminating of
urban mobility data face challenges such as data collection, handling of missing information,
and privacy protection. An alternative to tackle this problem is the generation of synthetic
data based on the original data, preserving its characteristics while maintaining its privacy.
Considering that urban mobility data are highly time-dependent, they may be represented as
time series. Thus, in this work, we propose MobDeep, a deep learning-based framework to
generate and evaluate urban mobility time series models. In this work, we use a statistical
model (ARIMA) and three deep learning-based models (GANs) to simulate time series. We
validate the proposed solution using an open dataset that contains information about bicycle
rentals in US cities and a private dataset that contains information about the urban traffic in
Vitória-ES, reported by the WAZE users. The evaluation results show that the proposed
solution using deep learning-based models can generate synthetic data with the same
characteristics as the real ones. With this approach, the models can be shared, allowing the
generation of synthetic data and preserving the privacy of the original dataset.

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