In this position paper we present S 2 -MOVE (Smart and Social Move), a social innovation project funded by MIUR (Italian Ministry of Education, Research and University). S 2 -MOVE is based on a new conception of urban community, that allows citizens to share data and decisions in a smart and innovative way. In order to implement this new vision of urban mobility, called smart mobility, a deep integration among citizens, private and public transportation systems and ICT is required.In this new social and challenging scenario, citizens are not just customers of a service, but they produce and share information to obtain a common goal: a smart and efficient mobility, for everyone. S 2 -MOVE proposes an architecture able to collect, update, and process real-time and heterogeneous information from various electronic devices (tablets, smartphones, electronic control devices in vehicles) and from the actors of the urban scenario (public/private transportation vehicles, pedestrians, infrastructures). Mining such information allows to produce new knowledge which is then made available again to the citizens through specific services. The urban mobility issues to which S 2 -MOVE can offer a contribution, are linked to two crucial aspects: continuous and shared monitoring for real-time management of urban traffic and an interactive mobile information service for drivers. In this position paper, after introducing the rationale of the project, we describe the main technological aspects involved in the design and in the implementation of the S 2 -MOVE architecture. Then, we present a proof of the control approach used to create and manage fleets of vehicles and we provide some preliminary results obtained using simulation.
Troubleshooting complex systems, such as industrial plants and machinery, is a task entailing an articulated decision making process hard to structure, and generally relying on human experience. Recently probabilistic reasoning, and Bayesian networks in particular, proved to be an effective means to support and drive decisions in Troubleshooting. However, troubleshooting a real system requires to face scalability and feasibility issues, so that the direct employment of Bayesian networks is not feasible. In this paper we report our experience in applying Bayesian approach to industrial case and we propose a methodology to decompose a complex problem in more treatable parts.
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