Abnormal Events Detection in Traffic Data

Xiao Bing Hu

doi:10.4028/www.scientific.net/AMR.779-780.525

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 779-780Abnormal Events Detection in Traffic Data

Abnormal Events Detection in Traffic Data

Abstract:

Detecting abnormal events such as crash is a practical problem that is important to Intelligent Transportation System. By taking advantage of the data recorded by the remote sensors which are deployed along the road, we can perform data mining techniques to see whether there are abnormal events happening on the road. This paper aims at proposing an abnormal-events-detecting method based on the traffic data, which first utilizes outlier detection to generate a fuzzy result set from source data, and then through the time series mining techniques to filter that to obtain an accurate experimental one. Experiment with real-world data shows that our method works satisfactorily in detecting abnormalities such crash, stall and hazard on the road.

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Periodical:

Advanced Materials Research (Volumes 779-780)

Pages:

525-529

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.779-780.525

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Online since:

September 2013

Authors:

Xiao Bing Hu

Keywords:

Data Mining (DM), Outlier Detection, Pattern Recognition, Traffic Pattern

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