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.


    Access

    Access via TIB


    Export, share and cite



    Title :

    ST TD outlier detection


    Contributors:
    Sun, Dihua (author) / Zhao, Hongzhuan (author) / Yue, Hang (author) / Zhao, Min (author) / Cheng, Senlin (author) / Han, Weijian (author)

    Published in:

    Publication date :

    2017-05-01


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    ST TD outlier detection

    Sun, Dihua / Zhao, Hongzhuan / Yue, Hang et al. | Wiley | 2017

    Free access

    Saliency modeling via outlier detection

    Chen, C. / Tang, H. / Lyu, Z. et al. | British Library Online Contents | 2014


    Damage detection using outlier analysis

    Worden, K. | Online Contents | 2000


    Cluster-Based Flight Trajectory Outlier Detection

    Mikol Forney / Banavar Sridhar / Kenneth Freeman | NTRS


    Outlier Detection for Distributed Pressure Measurements

    Barklage, Alexander / Reimer, Lars / Bekemeyer, Philipp | AIAA | 2024