ST TD outlier detection. Issue 4 (13th March 2017)

• Record Type: Journal Article
• Authors: Sun, Dihua; Zhao, Hongzhuan; Yue, Hang; Zhao, Min; Cheng, Senlin; Han, Weijian
• Is Part Of: IET intelligent transport systems. Volume 11:Issue 4(2017)
• Page Start: 203
• Page End: 211
• Publication Date: 2017-03-13
• Subjects: cyber‐physical systems -- intelligent transportation systems; innovative firefly algorithm -- TD synchronisation methods -- IFA‐STODM -- FA‐based ST outlier detection mechanism -- transportation cyber physical system -- traffic data -- vehicle‐to‐everything traffic information propagation; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2016.0261
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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. 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.