Most works on graph neural networks (GNNs) for traffic speed prediction assume near-complete data and little variance of base speed levels. However, both assumptions do not necessarily hold true for network-wide probe vehicle data (PVD). Therefore, we applied two state-of-the-art GNNs to sparse PVD from a road network with highly varying speed levels and to dense motorway data for comparison. We introduce two methods to adapt preexisting GNNs for improved prediction performance: normalization of speed values with respect to the base speed levels of different roads led to significant improvements of prediction performance on both datasets. Using the number of observations supporting each speed value as an additional input feature can improve prediction performance. Furthermore, we identified characteristics of data and models encouraging the use of either method. As no fitting dataset to evaluate these approaches was found, a novel dataset derived from PVD is introduced. It features sparse speed values and underlying numbers of observations for a road network with varying speed levels.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Sparse Data Traffic Speed Prediction on a Road Network With Varying Speed Levels


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Beeking, Moritz (Autor:in) / Steinmaßl, Markus (Autor:in) / Urban, Melanie (Autor:in) / Rehrl, Karl (Autor:in)


    Erscheinungsdatum :

    01.01.2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Urban road network traffic speed prediction method and related equipment

    CHEN JUN / CHAI BIN / WANG YUDONG | Europäisches Patentamt | 2025

    Freier Zugriff

    Road traffic speed prediction method fused with multi-feature neural network

    XING XUE / MU TIAN'AO | Europäisches Patentamt | 2023

    Freier Zugriff

    ROAD TRAFFIC SPEED PREDICTION METHOD FUSING MULTI-FEATURE NEURAL NETWORK

    XING XUE | Europäisches Patentamt | 2024

    Freier Zugriff

    Computerized traffic speed measurement using sparse data

    ZHENG YU / CHANG ERIC | Europäisches Patentamt | 2020

    Freier Zugriff

    GraphSAGE-Based Traffic Speed Forecasting for Segment Network With Sparse Data

    Liu, Jielun / Ong, Ghim Ping / Chen, Xiqun | IEEE | 2022