Scalable sparse bayesian network learning for spatial applications

Traffic routes through a street network contain patterns and are no random walks. Such patterns exist for instance along streets or between neighbouring street segments. The extraction of these patterns is a challenging task due to the enormous size of city street networks, the large number of required training data and the unknown distribution of the latter. We apply Bayesian Networks to model the correlations between the locations in space-time trajectories and address the following tasks. We introduce and examine a Bayesian Network Learning algorithm enabling us to handle the complexity and performance requirements of the spatial context. Furthermore, we apply our method to German cities, evaluate the accuracy and analyse the runtime behaviour for different parameter settings.