Patterns of traffic flow trajectories play an essential role in analysing traffic monitoring data in transportation studies. This research presents a data-adaptive clustering approach to explore traffic flow patterns and a unified algorithm to impute missing values for incomplete traffic flow trajectories. We recommend using subspace-projected functional data clustering with the assumption that each observed daily traffic flow trajectory is a realization of a random function sampled from a mixture of stochastic processes, and each subprocess represents a cluster subspace spanned by the mean function and eigenfunctions of the covariance kernel of the random trajectories. The unified algorithm combines probabilistic functional clustering with functional principal component analysis to propose a mixture prediction for missing value imputation. The proposed methods effectively unravel distinctive daily traffic flow patterns and improve the accuracy of missing value imputation. The advantage of the proposed approaches is demonstrated through numerical studies of a real traffic flow data application.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Functional clustering and missing value imputation of traffic flow trajectories


    Beteiligte:
    Li, Pai-Ling (Autor:in) / Chiou, Jeng-Min (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2021-01-01


    Format / Umfang :

    21 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    A functional data approach to missing value imputation and outlier detection for traffic flow data

    Chiou, Jeng-Min / Zhang, Yi-Chen / Chen, Wan-Hui et al. | Taylor & Francis Verlag | 2014


    A clustering-based approach for data-driven imputation of missing traffic data

    Ku, Wei Chiet / Jagadeesh, George R. / Prakash, Alok et al. | IEEE | 2016


    Missing Value Imputation for the Analysis of Incomplete Traffic Accident Data

    Deb, Rupam / Liew, Alan Wee-Chung | Tema Archiv | 2014


    Missing traffic data: comparison of imputation methods

    Li, Yuebiao / Li, Zhiheng / Li, Li | Wiley | 2014

    Freier Zugriff

    Missing traffic data: comparison of imputation methods

    Li, Yuebiao / Li, Zhiheng / Li, Li | IET | 2014

    Freier Zugriff