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 proceedingConference contribution

213 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Pages1010-1018
Number of pages9
DOIs
Publication statusPublished - 16 Sep 2011
Event17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11 - San Diego, CA, United States
Duration: 21 Aug 201124 Aug 2011

Publication series

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

Other

Other17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
CountryUnited States
CitySan Diego, CA
Period21/8/1124/8/11

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Keywords

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

ASJC Scopus subject areas

  • Software
  • Information Systems

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). https://doi.org/10.1145/2020408.2020571