Abstract
Traffic congestion is a condition of a segment in the road network where the traffic demand is greater than the available road capacity. The detection of unusual traffic patterns including congestions is a significant research problem in the data mining and knowledge discovery community. However, to the best of our knowledge, the discovery of propagations, or causal interactions among detected traffic congestions has not been appropriately investigated before. In this research, we introduce algorithms which construct causality trees from congestions and estimate their propagation probabilities based on temporal and spatial information of the congestions. Frequent sub-structures of these causality trees reveal not only recurring interactions among spatio-temporal congestions, but potential bottlenecks or flaws in the designs of existing traffic networks. Our algorithms have been validated by experiments on a travel time data set recorded from an urban road network.
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