Random walks on graphs and the congestion transition in transport systems

Modeling traffic dynamics has highlighted some universal properties of emergent phenomena, like the stop and go congestion when the vehicle density overcomes a certain threshold. The congestion formation on a urban road network is one of the main issues for the development of a sustainable mobility in the future smart cities and different models have been proposed. The quantification of the congestion degree for a city has been considered by various authors and data driven models have been develpoed using the large data sets on individual mobility provided by the Information Communication Technologies. However the simulation results suggest the existence of universal features for the transition to global congested states on a road network. We cope with the question if simple transport models on graph can reproduce universal features of congestion formation and the existence of control parameters is still an open problem. We propose a reductionist approach to this problem studying a simple transport model on a homogeneous road network by means of a random process on a graph. Each node represents a location and the links connect the different locations. We assume that each node has a finite transport capacity and it can contain a finite number of particles (vehicles). The dynamics is realized by a random walk on graphs where each node has a finite flow and move particles toward the connected nodes according to given transition rates (link weights). Each displacement is possible if the number of particles in the destination nodes is smaller than their maximal capacity. The graph structure can be very simple, like a uniform grid, but we have also considered random graphs with maximum in and out degree, to simulate more realistic transport networks. We study the properties the stationary distributions of the particles on the graph and the possibility of the applying the entropy concept of Statistical Mechanics to characterize the stationary distributions and to understand the congestion formation.