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