ST TD outlier detection

Dihua Sun; Hongzhuan Zhao; Hang Yue; Min Zhao; Senlin Cheng; Weijian Han

ST TD outlier detection

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Author(s): Dihua Sun ^{1, 2} ; Hongzhuan Zhao ^{1, 2, 3} ; Hang Yue ⁴ ; Min Zhao ^{1, 2} ; Senlin Cheng ^{1, 2} ; Weijian Han ⁵
- Affiliations: 1: School of Automation, Chongqing University, Chongqing, People's Republic of China ;
  2: Key Laboratory of Cyber Physical Social Dependable Service Computation, Chongqing University, Chongqing, People's Republic of China ;
  3: School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin, Guangxi, People's Republic of China ;
  4: Johns Hopkins Healthcare LLC, Glen Burnie, MD, USA ;
  5: School of Automotive Engineering, Chongqing University, Chongqing, People's Republic of China
Source: Volume 11, Issue 4, May 2017, p. 203 – 211
DOI: 10.1049/iet-its.2016.0261 , Print ISSN 1751-956X, Online ISSN 1751-9578

Received 14/10/2016, Accepted 02/02/2017, Revised 28/12/2016, Published 03/02/2017

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.

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