On mining anomalous patterns in road traffic streams

Linsey Xiaolin Pang, Sanjay Chawla, Wei Liu, Yu Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

71 Scopus citations

Abstract

Large number of taxicabs in major metropolitan cities are now equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this paper we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area. We adapt likelihood ratio tests (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in very accurate and rapid detection of anomalous behavior.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 7th International Conference, ADMA 2011, Proceedings
Pages237-251
Number of pages15
EditionPART 2
DOIs
StatePublished - 2011
Externally publishedYes
Event7th International Conference on Advanced Data Mining and Applications, ADMA 2011 - Beijing, China
Duration: Dec 17 2011Dec 19 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7121 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Advanced Data Mining and Applications, ADMA 2011
Country/TerritoryChina
CityBeijing
Period12/17/1112/19/11

Keywords

  • Spatio-temporal outlier
  • emerging
  • persistent
  • upper-bounding

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