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Resilience and sustained adaptability in urban transport systems (UTS) Today, enhancing resilience in Urban Transport Systems is considered imperative for two main reasons: a) such systems provide critical support to every socio-economic activity and are currently themselves one of the most importa

— Smart City frameworks address new challenges to improve efficiency and sustainability of services for citizens, providing additional features and allowing the city environment to adaptively configure according to collected data and information.

Smart cities are providing advanced services gathering data from different sources. Cities collect static data like road graphs, service description as well as dynamic/real time data like weather forecast, traffic sensors, bus positions, events, emergency data, etc.

Today’s secure railway transportation management systems are employing more “intelligent”, highly computerized technology but are still strictly dependent on the site-specific configuration of track layout, which follows different rules for different nations.

Modeling cloud knowledge can be the basis for enabling a large range of reasoning applications. The modeling of cloud knowledge base includes ontologies for cloud model representation at level of IaaS, SaaS and PaaS with all details regarding cloud resources.

i-cities, CINI, Benevento 2016

i-cities, CINI, Benevento 2016

Allocation changes on cloud are complex and time consuming tasks, on cloning, scaling, etc. A solution to cope with these aspects is to perform a simulation.

Signaling systems grant safety routes for railway transportation. Recently, the need of interoperable systems among different European countries increases in consequence of the increment of trades. This leads to data integration challenges.

The main technical issues regarding smart city solutions are related todata gathering, aggregation, reasoning, access, and service delivering via Smart City APIs (Application Program Interfaces).