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

Predicting Network Congestion by Extending Betweenness Centrality to Interacting Agents