| dc.description.abstract | Urban mobility aspects have become a challenge with the constant growth of the global population. As a consequence of this increase, more data has become available, which allows new information technologies to improve the mobility systems, especially the transportation system. Thus, a low-cost strategy to handle these issues rises as an Intelligent Transportation System (ITS) concept. 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. In this proposal, we aim to use data from a vast number of sources to provide directions to improve the current mobility in cities. However, a substantial challenge emerges when we combine multiple data sources, increasing the spatiotemporal coverage issues which affect the development of Smart Mobility (SM) solutions. In this sense, we investigate solutions to improve the transportation system data quality, providing applications and services, enabling Intra-Vehicular Data (IVD) and Extra-Vehicular Data (EVD) fusion to improve mobility. We design a heterogeneous data fusion platform for SM, aiming to analyze each data type from the Vehicular Data Space (VDS), considering its spatiotemporal aspects. We introduced the concept of VDS, which map the data available and used by the community to design solutions for ITS. After that, we develop a set of approaches to fuse various datasets in benefit of SM. Initially, we conducted studies to fusing IVD saving fuel, reducing emissions and ensuring the security of car-sharing in Vehicular Ad-hoc Networks (VANETs). Moreover, fusing the EVD, we developed a model, based on social media data to enrich the current traffic information, offering more options to people moves in a city. Finally, we developed an approach to fusing Intra-Extra-Vehicular Data (IEVD), allowing to enhance the road traffic data quality and enriching the current spatiotemporal data coverage. | |
