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Outlier mining based automatic incident detection on urban arterial road

  • Beihang University

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

Abstract

Nowadays, Floating Car Data (FCD), which is becoming an important way to acquire traffic information, has been widely taken to estimate speed or travel time on road. In this paper, we introduce the concept of outlier mining into Automatic Incident Detection (AID) based on FCD and propose a novel AID approach on urban arterial road. According to the characteristics of incident, feature vector is selected from both spatial analysis and temporal analysis. Then a multilevel detection method that consists of filtering, outlier detection, and delay monitoring is proposed. The evaluation on real incident data and FCD gives the result that DR = 81.5% while FAR = 1.83%, which proves that the approach can achieve considerable effectiveness.

Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Mobile Technology, Application and Systems, Mobility '09
DOIs
StatePublished - 2009
Event6th International Conference on Mobile Technology, Application and Systems, Mobility '09 - Nice, France
Duration: 2 Sep 20094 Sep 2009

Publication series

NameProceedings of the 6th International Conference on Mobile Technology, Application and Systems, Mobility '09

Conference

Conference6th International Conference on Mobile Technology, Application and Systems, Mobility '09
Country/TerritoryFrance
CityNice
Period2/09/094/09/09

Keywords

  • Automatic incident detection
  • Floating car data
  • Outlier mining
  • Urban arterial road

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