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Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams

Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xing Xie


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.


Publication typeInproceedings
Published inSIGKDD 2011
PublisherAssociation for Computing Machinery, Inc.

Newer versions

Linsey Xiaolin Pang, Sanjay Chawla, Wei Liu, and Yu Zheng. On Mining Anomalous Patterns in Road Traffic Streams, IEEE, 17 December 2011.

Nicholas Jing Yuan and Yu Zheng. Segmentation of Urban Areas Using Road Networks, Microsoft Technical Report, July 2012.

Previous versions

Yu Zheng. T-Drive trajectory data sample, 12 August 2011.

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