Aggregation and travel time calculation over large scale traffic networks: An empiric study on the Grenoble City

In this paper we consider aggregation technique to reduce the complexity of large-scale traffic network. In particular, we consider the city of Grenoble and show that, by clustering adjacent sections based on a similarity of speed condition, it is possible to cut down the complexity of the network without loosing crucial and intrinsic information. To this end, we consider travel time computation as a metric of comparison between the original graph and the reduced one: for each cluster we define four attributes (average speed, primary and secondary length and heading) and show that, in case of an aggregation rate of 95%, these attributes are sufficient in order to maintain the travel time error below the 25%.