Open AccessST TD outlier detection
Author(s) -
Sun Dihua,
Zhao Hongzhuan,
Yue Hang,
Zhao Min,
Cheng Senlin,
Han Weijian
Publication year - 2017
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2016.0261
Subject(s) - anomaly detection , outlier , computer science , artificial intelligence , computer vision , pattern recognition (psychology)
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.