Congestion transition on random walks on graphs
Lorenzo Di Meco, Mirko Degli Esposti, Federico Bellisardi, Armando, Bazzani

TL;DR
This paper models congestion formation in urban networks using a random walk framework on graphs, revealing universal properties and the role of fluctuations as precursors to congestion.
Contribution
It introduces an analytical approach to characterize traffic load distribution and congestion emergence using a maximum entropy principle and compares it with numerical simulations.
Findings
Congestion emerges as a percolation-like transition with increasing load.
Synchronous dynamics induce correlations that cluster empty and congested nodes.
Traffic load fluctuations can serve as early indicators of congestion.
Abstract
The congestion formation on a urban road network is one of the key issue for the development of a sustainable mobility in the future smart cities. In this work we propose a reductionist approach studying the stationary states of a simple transport model using of a random process on a graph, where each node represents a location and the weight links give the transition rates to move from one node to another that represent the mobility demand. Each node has a finite transport capacity and a maximum load capacity and we assume that the average. In the approximation of the single step process we are able to analytically characterize the traffic load distribution on the single nodes, using a local Maximum Entropy Principle. Our results explain how the congested nodes emerge when the total traffic load increases in analogous way to a percolation transition where the appearance of a congested…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Stochastic processes and statistical mechanics
