Traffic status evaluation is the most important part of Intelligent Traffic System (ITS). However, nowadays, most models used to identify traffic performance are either based on traffic flow theories or machine learning algorithms and data mining algorithms. On that base, a new traffic performance evaluation model which combines traffic theory and machine learning algorithm is presented. In order to establish such model, characteristics of traffic data is discussed at first. The result of statistical analysis of traffic data shows that the probability density of a large amount of traffic data could be synthetized by a few Gaussian distributions. Therefore, a suitable traffic status evaluation model which combines Gaussian mixture model (GMM) and linear regression (LR) is proposed in this paper. In this model, firstly, by using Gaussian mixture model, historical traffic data will be clustered into three groups with respect to three traffic states, which are free flow, quasi-free flow and crowd flow. Then, the data with respect to crowd flow will be used to train linear regression model, and linear regression model will generate a straight line which analogues the line J in Kerner's traffic theory to classify crowd flow into two parts, which are related to synchronized flow and traffic jam. Through the two steps, traffic performance is described into four states, which are free flow, quasi-free flow, synchronized flow and traffic jam. The result of designed experiment shows that the model could evaluate traffic performance effectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-Time Traffic Status Classification Based on Gaussian Mixture Model


    Beteiligte:
    Liu, Xiong (Autor:in) / Pan, Li (Autor:in) / Sun, Xiaoliang (Autor:in)


    Erscheinungsdatum :

    01.06.2016


    Format / Umfang :

    445420 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-time traffic status broadcasting method

    JI GANG / HUANG JIAQIAN / WANG GUOQING et al. | Europäisches Patentamt | 2015

    Freier Zugriff

    MULTI-DIMENSIONAL URBAN TRAFFIC ANOMALY EVENT RECOGNITION METHOD BASED ON TERNARY GAUSSIAN MIXTURE MODEL

    WU CHAOTENG / ZHANG LU / GAO XIAO et al. | Europäisches Patentamt | 2021

    Freier Zugriff


    Gaussian Mixture Model-Based Speed Estimation and Vehicle Classification Using Single-Loop Measurements

    Lao, Yunteng / Zhang, Guohui / Corey, Jonathan et al. | Taylor & Francis Verlag | 2012


    Method for recognizing multi-dimensional anomalous urban traffic event based on ternary gaussian mixture model

    WU CHAOTENG / ZHANG LU / GAO XIAO et al. | Europäisches Patentamt | 2022

    Freier Zugriff