This study develops non-parametric models to predict traffic conflicts at signalized intersections at the signal cycle level using machine learning approaches. Three different datasets were collected, one from Surrey, Canada, and the other two from Los Angeles and Georgia, U.S.A. From the datasets, traffic conflicts measured by modified time to collision and traffic parameters such as traffic volume, shockwave area, platoon ratio, and shockwave speed were extracted. Multilayer perceptron (MLP), support vector regression (SVR), and random forest (RF) models were developed based on the Surrey dataset, and the Bayesian optimization approach was adopted to optimize the model hyperparameters. The optimized models were applied to the Los Angeles and Georgia datasets to test their transferability, and they were also compared to a traditional safety performance function (SPF) developed using negative binominal regression. The results show that all the three Bayesian optimized machine learning models have high predictive accuracy and acceptable transferability, and the MLP model is a little better than the SVR and RF models. In addition, all three models outperform the traditional SPF with regard to predictive accuracy. The model sensitivity analysis also show that the traffic volume and shockwave area have positive effects on traffic conflicts, while the platoon ratio has negative effects.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Conflict Prediction at Signal Cycle Level Using Bayesian Optimized Machine Learning Approaches


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Zheng, Lai (Autor:in) / Hu, Zhenlin (Autor:in) / Sayed, Tarek (Autor:in)


    Erscheinungsdatum :

    10.10.2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic Flow Breakdown Prediction using Machine Learning Approaches

    Filipovska, Monika / Mahmassani, Hani S. | Transportation Research Record | 2020





    Environmentally-Optimized Air Traffic Conflict Resolutions

    Visser, H. | British Library Conference Proceedings | 2007