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Detection of Outliers in a Time Series of Available Parking Spaces

Lookup NU author(s): Dr Yanjie Ji, Dr Amy Guo, Professor Phil Blythe

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Abstract

With the provision of any source of real-time information, the timeliness and accuracy of the data provided are paramount to the effectiveness and success of the system and its acceptance by the users. In order to improve the accuracy and reliability of parking guidance systems (PGSs), the technique of outlier mining has been introduced for detecting and analysing outliers in available parking space (APS) datasets. To distinguish outlier features from the APS’s overall periodic tendency, and to simultaneously identify the two types of outliers which naturally exist in APS datasets with intrinsically distinct statistical features, a two-phase detection method is proposed whereby an improved density-based detection algorithm named “local entropy based weighted outlier detection” (EWOD) is also incorporated. Real-world data from parking facilities in the City of Newcastle upon Tyne was used to test the hypothesis. Thereafter, experimental tests were carried out for a comparative study in which the outlier detection performances of the two-phase detection method, statistic-based method, and traditional density-based method were compared and contrasted. The results showed that the proposed method can identify two different kinds of outliers simultaneously and can give a high identifying accuracy of 100% and 92.7% for the first and second types of outliers, respectively.


Publication metadata

Author(s): Ji Y, Tang D, Guo W, Blythe PT, Ren G

Publication type: Article

Publication status: Published

Journal: Mathematical Problems in Engineering

Year: 2013

Volume: 2013

Print publication date: 01/03/2013

ISSN (print): 1024-123X

ISSN (electronic): 1563-5147

Publisher: Hindawi Publishing Corporation

URL: http://dx.doi.org/10.1155/2013/416267

DOI: 10.1155/2013/416267

Notes: Special Issue on Fuzzy Computing and Intelligent Transportation. Article no. 416267 is 12 pp.


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