  The idea of modern urban systems and smart cities requires monitoring and
careful analysis of different signals. Such signals can originate from
different sources and one of the most promising is the BTS, i.e. base
transceiver station, an element of mobile carrier networks. This paper presents
the fundamental problems of elicitation, classification and understanding of
such signals so as to develop context-aware and pro-active systems in urban
areas. These systems are characterized by the omnipresence of computing which
is strongly focused on providing on-line support to users/inhabitants of smart
cities. A method of analyzing selected elements of mobile phone datasets
through understanding inhabitants' behavioral fingerprints to obtain smart
scenarios for public transport is proposed. Some scenarios are outlined. A
multi-agent system is proposed. A formalism based on graphs that allows
reasoning about inhabitant behaviors is also proposed.
