  In a motorway network, correlations between parts or, more precisely, between
the sections of (different) motorways, are of considerable interest. Knowledge
of flows and velocities on individual motorways is not sufficient, rather,
their correlations determine or reflect, respectively, the functionality of and
the dynamics on the network. These correlations are time-dependent as the
dynamics on the network is highly non-stationary. Apart from the conceptual
importance, correlations are also indispensable to detect risks of failure in a
traffic network. Here, we proceed with revealing a certain hierarchy of
correlations in traffic networks that is due to the presence and to the extent
of collectivity. In a previous study, we focused on the collectivity motion
present in the entire traffic network, i.e. the collectivity of the system as a
whole. Here, we manage to subtract this dominant effect from the data and
identify the subdominant collectivities which affect different, large parts of
the traffic network. To this end, we employ a spectral analysis of the
correlation matrix for the whole system. We thereby extract information from
the virtual network induced by the correlations and map it on the true
topology, i.e. on the real motorway network. The uncovered subdominant
collectivities provide a new characterization of the traffic network. We carry
out our study for the large motorway network of North Rhine-Westphalia (NRW),
Germany.
