Fusion on Vehicular Data Space: An Approach to Smart Mobility

• Paulo Henrique Lopes Rettore Fraunhofer FKIE http://orcid.org/0000-0002-5491-7274
• Guilherme Maia Universidade Federal de Minas Gerais http://orcid.org/0000-0001-9758-7829
• Leandro A Villas Universidade de Campinas http://orcid.org/0000-0002-3372-3366
• Antonio A. F. Loureiro Universidade Federal de Minas Gerais http://orcid.org/0000-0002-5250-1785

DOI: https://doi.org/10.5753/sbrc_estendido.2020.12418

Resumo

Urban mobility aspects have become a challenge with the constant growth of the global population. As a consequence of such increase, more data has become available, which allows new information technologies to improve the mobility systems, especially the Intelligent Transportation System (ITS). However, the development of new applications and services for the ITS environment, improving the mobility, depending on the availability of vast amounts of data, despite its currently slow availability. This thesis aims to explore data from a vast number of sources from the ITS context to provide directions to improve mobility in cities. However, a substantial challenge emerges when we combine multiple data sources, increasing the data aspects as spatiotemporal coverage, which affects the development of Smart Mobility (SM) solutions. In this sense, we investigate solutions to improve the data quality of transportation systems, providing applications and services, enabling Intra-Vehicle Data (IVD) and Extra-Vehicle Data (EVD) fusion to enrich the raw data. We design a heterogeneous data fusion platform to SM, aiming to fuse those data considering their aspects, highlighting the most relevant methods and techniques to achieve the application goals.

Palavras-chave: Intelligent Transportation Systems, Heterogeneous Data Fusion, Smart Mobility

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