We put forward architecture of a framework for integration of data from moving objects related to urban transportation network.\nMost of this research refers to the GPS outdoor geolocation technology and uses distributed cloud infrastructure with big data\nNoSQL database. A network of intelligent mobile sensors, distributed on urban network, produces congestion traffic patterns.\nCongestion predictions are based on extended simulationmodel. Thismodel provides trafficindicators calculations,which fusewith\nthe GPS data for allowing estimation of traffic states across the whole network. The discovery process of congestion patterns uses\nsemantic trajectories metamodel given in our previous works. The challenge of the proposed solution is to store patterns of traffic,\nwhich aims to ensure the surveillance and intelligent real-time control network to reduce congestion and avoid its consequences.\nThe fusion of real-time data from GPS-enabled smartphones integrated with those provided by existing traffic systems improves\ntraffic congestion knowledge, as well as generating new information for a soft operational control and providing intelligent added\nvalue for transportation systems deployment.
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