  We present a simple model to predict network activity at the edge level, by
extending a known approximation method to compute Betweenness Centrality (BC)
with a repulsive mechanism to prevent unphysical densities. By taking into
account the strong interaction effects often observed in real phenomena, we aim
to obtain an improved measure of edge usage during rush hours as traffic
congestion patterns emerge in urban networks. In this approach, the network is
iteratively populated by agents following dynamically evolving fastest paths,
that are progressively attracted towards uncongested parts of the network, as
the global traffic volume increases. Following the transition of the network
state from empty to saturated, we study the emergence of congestion and the
progressive disruption of global connectivity due to a relatively small
fraction of crowded edges.
  We assess the predictive power of our model by comparing the speed
distribution against a large experimental dataset for the London area with
remarkable results, which also translate into a qualitative similarity of the
congestion maps. Also, percolation analysis confirms a quantitative agreement
of the model with the real data for London. For seven other topologically
different cities we performed simulations to obtain the Fisher critical
exponent $\tau$ that showed no common functional dependence on the traffic
level. The critical exponent $\gamma$, studied to assess the power-law decay of
spatial correlation, was found inversely proportional to the number of vehicles
both for real and simulated traffic.
  This simulation approach seems particularly fit to describe qualitative and
quantitative properties of the network loading process, culminating in
peak-hour congestion, by using only topological and geographical network
features.
