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In this paper we propose a framework to acquire, store, manipulate and integrate information to optimize scheduling and traffic control in real-time, in an urban environment. This research concentrates on the architecture that supports this functionality and on the integration of technologies and algorithms to provide data analytics and decision making in real time. An outline of the research is presented together with the results obtained when the framework is applied to the Mermaid Technology company data, for a bus network operation in Copenhagen. The contribution of this work in progress is in offering a two-tier architecture that can deal with both the steady-state regime of the bus operation in an urban environment and with ad hoc, emergency situations.