Intelligent transportation systems (ITSs) gather information about traffic conditions by collecting data from a wide range of on-ground sensors. The collected data usually suffer from irregular spatial and temporal resolution. Consequently, missing data is a common problem faced by ITSs. In this paper, we consider the problem of missing data in large and diverse road networks. We propose various matrix and tensor based methods to estimate these missing values by extracting common traffic patterns in large road networks. To obtain these traffic patterns in the presence of missing data, we apply fixed-point continuation with approximate singular value decomposition, canonical polyadic decomposition, least squares, and variational Bayesian principal component analysis. For analysis, we consider different road networks, each of which is composed of around 1500 road segments. We evaluate the performance of these methods in terms of estimation accuracy, variance of the data set, and the bias imparted by these methods.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Matrix and Tensor Based Methods for Missing Data Estimation in Large Traffic Networks




    Publication date :

    2016




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.84 Straßenverkehr / 55.24 / 55.84 / 55.24 Fahrzeugführung, Fahrtechnik



    Matrix and Tensor Based Methods for Missing Data Estimation in Large Traffic Networks

    Asif, Muhammad Tayyab / Mitrovic, Nikola / Dauwels, Justin et al. | IEEE | 2016



    Missing Traffic Data Imputation based on Tensor Completion and Graph Network Fusion

    Xia, Chengliang / Yin, Xiang / Yu, Junyang et al. | Transportation Research Record | 2025



    Convolutional Low-Rank Tensor Representation for Structural Missing Traffic Data Imputation

    Li, Ben-Zheng / Zhao, Xi-Le / Chen, Xinyu et al. | IEEE | 2024