Compute efficiency is an important consideration for traffic flow prediction models. Machine learning algorithms adjust model parameters automatically based on the data, but often require users to set additional parameters, known as hyperparameters. Hyperparameters can significantly impact prediction accuracy. Traffic measurements, typically collected online by sensors, are serially correlated. Moreover, the data distribution may change gradually. A typical adaptation strategy is periodically re-tuning the model hyperparameters, at the cost of computational burden. In this work, we present an efficient and principled online hyperparameter learning algorithm for kernel-based traffic prediction models. In tests with real traffic measurement data, our approach requires as little as one-seventh of the computation time of other tuning methods, while achieving better or similar prediction accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Efficient Online Hyperparameter Learning for Traffic Flow Prediction


    Beteiligte:
    Zhan, Hongyuan (Autor:in) / Gomes, Gabriel (Autor:in) / Li, Xiaoye S. (Autor:in) / Madduri, Kamesh (Autor:in) / Wu, Kesheng (Autor:in)


    Erscheinungsdatum :

    01.11.2018


    Format / Umfang :

    486014 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Online prediction method for traffic flow

    ZHAO PENG / ZHOU ZHIHUA / QIAN YUYANG | Europäisches Patentamt | 2024

    Freier Zugriff

    REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting

    Liu, Qingxiang / Sun, Sheng / Liang, Yuxuan et al. | IEEE | 2025



    Online space-time traffic flow prediction method

    XIAO JIANHUA / XIAO HONGBO / DING LIMING et al. | Europäisches Patentamt | 2024

    Freier Zugriff