Real-time (online) road network traffic state estimation plays a vital role in enhancing the services offered by Intelligent Transportation Systems (ITS). Spatio-temporal data vacancies of contemporary acquisition systems considerably limit the reliability of online traffic state estimation. Online estimation insists smaller temporal frame widths inhibiting the accuracies of low rank matrix reconstruction approaches, while matrix imputation methods do not capture the non-trivial traffic data relationships. Alternatively, this paper investigates traffic state estimation by constructing sparse representations via Sparse Bayesian Learning (SBL) and Block SBL (BSBL) approaches to accommodate under-sampled data, independent of the acquisition type. Appropriate kernel matrices are determined by leveraging historical spatio-temporal correlations among the road network traffic data. Subsequently, unavailable traffic states are estimated from predictive distributions. The estimates are further pruned by kalman filter that corroborates online processing. With the SBL approach, experiments on PeMS traffic data demonstrate less than 6% Normalized Mean Absolute Error (NMAE) for a Signal Integrity (SI) of 0.5. Compared to the state-of-the-art approaches, this value is significantly better. BSBL approach gives similar error performance as SBL despite a SI of 0.26, at the cost of increased computational time. The NMAE from kalman filtered SBL (SBL+K) approach is less than 3.5% in contrast to 4.5% from kalman filtered BSBL (KBSBL) approach, thus demonstrating SBL+K approach as a good compromise between NMAE and computational time, facilitating more accurate online traffic state estimation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Sparse Bayesian Learning Assisted Approaches for Road Network Traffic State Estimation


    Contributors:


    Publication date :

    2021-03-01


    Size :

    1183849 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Expressway road traffic state estimation method based on dynamic Bayesian network

    SUN DIHUA / ZHAO MIN / LIU WEINING et al. | European Patent Office | 2015

    Free access

    Coarse-grained assessment method for traffic state of sparse road network

    SUN DAN / LIU YIFEI / LU XIAOYU et al. | European Patent Office | 2022

    Free access


    Vehicle State Estimation within a Road Network using a Bayesian Filter

    Niedfeldt, Peter / Kingston, Derek / Beard, Randal | Tema Archive | 2011


    Intelligent traffic road network traffic flow prediction method based on Bayesian algorithm

    WANG MENGXIANG / WAN FUJUN / FU QIANG et al. | European Patent Office | 2024

    Free access