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Traffic congestion is one of the most notable problems arising in worldwideurban areas, importantly compromising human mobility and air quality. Currenttechnologies to sense real-time data about cities, and its open distributionfor analysis, allow the advent of new approaches for improvement and control.Here, we propose an idealized model, the Microscopic Congestion Model, based onthe critical phenomena arising in complex networks, that allows to analyticallypredict congestion hotspots in urban environments. Results on real cities' roadnetworks, considering, in some experiments, real-traffic data, show that theproposed model is capable of identifying susceptible junctions that mightbecome hotspots if mobility demand increases.

A model to identify urban traffic congestion hotspots in complex networks