Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams

Detecting outliers in spatio-temporal traffic data is an important research problem in data mining and knowledge discovery due to the increasing amount of spatio-temporal data available and the need to understand and interpret it. However, the discovery of relationships, especially causal interactions, among detected traffic outliers has not been sufficiently studied. To address the lack of this research, in this paper we propose algorithms which construct outlier causality trees based on temporal and spatial properties of detected outliers. Frequent substructures of these causality trees reveal not only interactions among spatio-temporal outliers, but potential drawbacks in existing design of traffic networks. Effectiveness and strength of our algorithms are validated by experiments on a very large volume of real taxi trajectories in an urban road network.