Data Fusion on Intelligent Transportation Systems

Urban mobility deals with the movement of people and cargo in urban environments and has become a challenge with the constant growth of the global population. As a consequence of such an 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 to improve mobility depends on the availability of vast amounts of data.

This investigation aims to explore data from a vast number of sources from the ITS context to provide directions to improve mobility in urban scenarios. 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 raw data.

We focus on designing a heterogeneous data fusion platform for SM, aiming to fuse the data considering their aspects, highlighting the most relevant methods and techniques to achieve the application goals.