2011 DiscoveringSpatioTemporalCausal

• (Liu et al., 2011) ⇒ Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xie Xing. (2011). “Discovering Spatio-temporal Causal Interactions in Traffic Data Streams.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020571

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Discovering+Spatio-temporal+Causal+Interactions+in+Traffic+Data+Streams
• http://dl.acm.org/citation.cfm?id=2020408.2020571&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; data mining; frequent sub-structures; outlier causalities; spatio-temporal outliers; urban computing and planning

Abstract

The detection of outliers in spatio-temporal traffic data is an important research problem in the data mining and knowledge discovery community. However to the best of our knowledge, the discovery of relationships, especially causal interactions, among detected traffic outliers has not been investigated before. 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 recurring interactions among spatio-temporal outliers, but potential flaws in the design of existing traffic networks. The effectiveness and strength of our algorithms are validated by experiments on a very large volume of real taxi trajectories in an urban road network.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 DiscoveringSpatioTemporalCausal	Wei Liu Yu Zheng Sanjay Chawla Jing Yuan Xie Xing			Discovering Spatio-temporal Causal Interactions in Traffic Data Streams				10.1145/2020408.2020571		2011