Congestion is the condition of the road in the traffic networks which is characterised as slow speed and long travel time. The detection of unusual traffic patterns including congestions is an significant research problem in the data mining and knowledge discovery community. However, to the best of our knowledge, the discovery of propagation, or causal interactions among detected traffic congestions has not been appropriately investigated before. In this research, we introduce algorithms which construct causality trees based on temporal and spatial information of identified congestions. Frequent substructures of these causality trees reveal not only recurring interactions among spatio-temporal congestions, but potential bottlenecks or flaws in the design of existing traffic networks. Our algorithms are validated by experiments on a large real-time travel time data in an urban road network.
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