@inproceedings{3ddde74b9ca148b89e07bbb145ff783e,
title = "Discovering spatio-temporal causal interactions in traffic data streams",
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.",
keywords = "Frequent substructures, Outlier causalities, Spatio-temporal outliers, Urban computing and planning",
author = "Wei Liu and Yu Zheng and Sanjay Chawla and Jing Yuan and Xing Xie",
year = "2011",
doi = "10.1145/2020408.2020571",
language = "English",
isbn = "9781450308137",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "1010--1018",
booktitle = "Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11",
address = "United States",
note = "17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011 ; Conference date: 21-08-2011 Through 24-08-2011",
}