What is it about?
This study develops some research approaches to detect spatiotemporal (ST) data outliers for the development of transportation systems. These research approaches include the theorisation of ST traffic outliers, the creation of an innovative firefly algorithm (IFA), the discussion of TD synchronisation methods and the development of the FA-based ST outlier detection mechanism (IFA-STODM). The experimental results show that the proposed IFA-STODM is an effective and efficient method for the detection of ST TD outliers.
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Why is it important?
Through vehicle-to-everything traffic information propagation often causes data outliers, due to data delay, data loss, inaccurate data and inconsistent data. Traffic data (TD) with outliers may incorrectly describe traffic conditions and decline the reliability and stability of transportation cyber physical system.
Perspectives
On the basis of the analysis of ST traffic characteristics, this paper theorises TST-outlier, discusses TD synchronisation problems and develops computer algorithms for ST TD outliers. In contrast to other methods (e.g. SODA and DODA), the experimental results demonstrate the proposed IFA-STODM can detect TST-outliers in a higher accuracy. Also, the new outlier approach is useful for traffic parameter estimation. This paper clearly explains the reasons why the outliers are produced, describes the characteristics of ST TD outliers, discovers the outliers over both of spatial and temporal dimensions and distinguishes outliers from normal data sets using statistical data analyses and computer algorithms.
Hongzhuan ZHAO
Guilin University of Electronic Technology
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This page is a summary of: ST TD outlier detection , IET Intelligent Transport Systems, May 2017, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-its.2016.0261.
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