An IoE and Big Multimedia Data approach for Urban Transport System management in Smart Resilient City

Today, the complexity of urban systems constraints the administrations to consider smart technologies and related huge amounts of data generated as a means to cope with unexpected events. This article aims at presenting an innovative full stack solution to exploit Internet of Everything and Big Multimedia Data in Smart City to manage resilience of Urban Transport Systems (UTS), which is one of the most critical infrastructures of the city. The approach is based on a novel data driven approach to Resilience Engineering and Functional Resonance Analysis method (FRAM), to understand and model an UTS in the context of Smart City and to support evidence driven decision making. The paper proposes an architecture taking into account: a) different kinds of available data generated in the Smart City, b) Big Data collection and semantic aggregation and enrichment; c) data sense-making process composed by analytics of different data sources like social media, communication networks, IoT, user behavior; d) tools for knowledge driven decisions able to combine different information generated by analytics, experience, and structural information of the city into a comprehensive and evidence driven decision model. The solution has been applied in Firenze metropolitan city in the context of RESOLUTE H2020 research project of the European Commission.