The paper explores the potential of Multi-Output Gaussian Processes to tackle network-wide travel time prediction in an urban area. Forecasting in this context is challenging due to the complexity of the traffic network, noisy data and unexpected events. We build on recent methods to develop an online model that can be trained in seconds by relying on prior network dependences through a coregionalized covariance. The accuracy of the proposed model outperforms historical means and other simpler methods on a network of 47 streets in Stockholm, by using probe data from GPS-equipped taxis. Results show how traffic speeds are dependent on the historical correlations, and how prediction accuracy can be improved by relying on prior information while using a very limited amount of current-day observations, which allows for the development of models with low estimation times and high responsiveness.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Urban network travel time prediction via online multi-output Gaussian process regression


    Beteiligte:


    Erscheinungsdatum :

    01.10.2017


    Format / Umfang :

    455065 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Urban Arterial Travel Time Prediction Using Support Vector Regression

    Philip, Anna Mary / Ramadurai, Gitakrishnan / Vanajakshi, Lelitha | Springer Verlag | 2018


    Real-Time Travel Time Prediction on Urban Traffic Network

    Y. Wagt / Y. J. Wu / X. Ma et al. | NTIS | 2010


    Travel-Time Prediction With Support Vector Regression

    Wu, C.-H. / Ho, J.-M. / Lee, D. T. et al. | British Library Conference Proceedings | 2004


    Travel-time prediction with support vector regression

    Chun-Hsin Wu, / Jan-Ming Ho, / Lee, D.T. | IEEE | 2004


    Travel time prediction with support vector regression

    Chun-Hsin Wu, / Chia-Chen Wei, / Da-Chun Su, et al. | IEEE | 2003