Modeling Urban Mobility for E-Governance with Low Energy Complexity

In this paper we design some characteristics of a possible E-governance system for the urban mobility. In particular we shortly present the complex city features and the background automata gas paradigm for a microscopic modeling. Moreover we show some statistical laws based on long time series of traffic dynamics data, fundamental for the now-casting and the private vehicles mobility control. We study also the crowd dynamics, specifying a stochastic equation founded on a cognitive decisional dynamics, with a threshold changing the evolution from a cooperative behavior to a selfish one, which we identify with the emergence of ‘panic states’. Concluding, essentially our E-governance system is composed of a large data base, of one or more centers that can collect real time microscopic data, elaborate models based on the individual mobility demand, find the optimal self-organized mobility states and diffuse the relative information (best path dynamical map) along the mobility network performing a bidirectional continuous interaction with the citizens. Moreover we underline that the complex interactions via information diffusion, i.e. cognitive based dynamics and mobility governance are low energy consuming