The challenge of bringing more intelligence to the infrastructure of modern cities requires a change of thinking and states a demand for new algorithms and strategies. One important outcome of those algorithms is a realtime estimation of the prevalent spatio-temporal conditions of public transportation networks by making use of distributed image processing on networked smart camera systems. This paper provides a detailed analysis of two exemplary networked applications that can use the derived data. A conducted simulation study based on the infrastructure of real cities shows the potential of using autonomously generated knowledge, that smart camera systems can provide. Especially, inter-camera object tracking, as well as adaptive and smart navigation tasks can benefit considerably and substantiate the need for autonomous and confidential image processing.