There have been limited efforts to investigate the potential of using detailed trajectory data obtained from connected vehicles and/or other sensors in deriving measures for use in real-time traffic state estimation. This study utilizes a hybrid machine learning approach that classifies the traffic states as a function of traffic disturbance and safety surrogate metrics estimated based on detailed trajectories combined with macroscopic traffic metrics. The investigated disturbance metrics are the number of oscillations, and a measure of disturbance duration based on the time exposed time to collisions. The study, first, used unsupervised clustering techniques to classify traffic states into “breakdown” and “non-breakdown” in terms of both mobility and safety. Then, the categorized traffic state was used as a binary response to the macroscopic and microscopic metrics, as features, to train supervised machine learning techniques for predicting traffic flow breakdown in the following 5-min interval in real-time operations. The study found that the utilizing disturbance and safety surrogate metrics in the real-time classification of traffic flow state increases the accuracy of prediction.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Freeway’s Traffic Flow Breakdown Prediction Utilizing Disturbance Metrics Based on Trajectory Data


    Beteiligte:

    Kongress:

    International Conference on Transportation and Development 2021 ; 2021 ; Virtual Conference



    Erscheinungsdatum :

    04.06.2021




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Using Traffic Disturbance Metrics to Estimate and Predict Freeway Traffic Breakdown and Safety Events

    Azizi, Leila / Hadi, Mohammed | Transportation Research Record | 2021


    Assessing trajectory prediction performance - metrics definition

    Mondoloni, S. / Swierstra, S. / Paglione, M. | IEEE | 2005



    Assessing Trajectory Prediction Performance - Metrics Definition

    Mondoloni, S. / Swierstra, S. / Paglione, M. et al. | British Library Conference Proceedings | 2005


    Utilizing Hybrid Trajectory Prediction Models to Recognize Highly Interactive Traffic Scenarios

    Zipfl, Maximilian / Spickermann, Sven / Zollner, J. Marius | IEEE | 2023