  We investigate the behavior of extended urban traffic networks within the
framework of percolation theory by using real and synthetic traffic data. Our
main focus shifts from the statistical properties of the cluster size
distribution studied recently, to the spatial analysis of the clusters at
criticality and to the definition of a similarity measure between whole urban
configurations. We discover that the breakup patterns of the complete network,
formed by the connected functional road clusters at criticality, show
remarkable stability from one hour to the next, and predictability for
different days at the same time. We prove this by showing how the average
spatial distributions of the highest-rank clusters evolve over time, and by
building a taxonomy of traffic states via dimensionality-reduction of the
distance matrix, obtained via a clustering similarity score. Finally, we show
that a simple random percolation model can approximate the breakup patterns of
heavy real traffic when long-ranged spatial correlations are imposed.
