Discovering spatio-temporal causal interactions in traffic data streams

Wei Liu; Yu Zheng; Sanjay Chawla; Jing Yuan; Xing Xie

doi:10.1145/2020408.2020571

Discovering spatio-temporal causal interactions in traffic data streams

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

288 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Publisher	Association for Computing Machinery
Pages	1010-1018
Number of pages	9
ISBN (Print)	9781450308137
DOIs	https://doi.org/10.1145/2020408.2020571
State	Published - 2011
Externally published	Yes
Event	17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011 - San Diego, United States Duration: Aug 21 2011 → Aug 24 2011

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011
Country/Territory	United States
City	San Diego
Period	08/21/11 → 08/24/11

Keywords

Frequent substructures
Outlier causalities
Spatio-temporal outliers
Urban computing and planning

Access to Document

10.1145/2020408.2020571

Cite this

Liu, W., Zheng, Y., Chawla, S., Yuan, J., & Xie, X. (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'11 (pp. 1010-1018). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/2020408.2020571

Liu, Wei ; Zheng, Yu ; Chawla, Sanjay et al. / Discovering spatio-temporal causal interactions in traffic data streams. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11. Association for Computing Machinery, 2011. pp. 1010-1018 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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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",

note = "17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011 ; Conference date: 21-08-2011 Through 24-08-2011",

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Liu, W, Zheng, Y, Chawla, S, Yuan, J & Xie, X 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'11. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 1010-1018, 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, San Diego, United States, 08/21/11. https://doi.org/10.1145/2020408.2020571

Discovering spatio-temporal causal interactions in traffic data streams. / Liu, Wei; Zheng, Yu; Chawla, Sanjay et al.
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11. Association for Computing Machinery, 2011. p. 1010-1018 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - Outlier causalities

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KW - Urban computing and planning

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Liu W, Zheng Y, Chawla S, Yuan J, Xie X. 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'11. Association for Computing Machinery. 2011. p. 1010-1018. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/2020408.2020571

Discovering spatio-temporal causal interactions in traffic data streams

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Keywords

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