The development of real-time traffic models is of paramount importance for the purposes of optimizing traffic flow. Inspired by the compositional model (CM) and the METANET model, this paper proposes an interval approach for macroscopic traffic modeling. We develop an interval CM (ICM) and an interval implementation of the METANET model (IMETANET) that provide a natural way of predicting traffic flows without the assumption of uniform distribution of vehicles in a cell. The interval macroscopic models are suitable for real-time applications in road networks and can be part of road traffic surveillance and control systems. The performances of the interval approaches are investigated for both the ICM and the IMETANET models. The efficiency of the interval models is demonstrated over simulated data, and as well as over real traffic data from Motorway Incident Detection and Automatic Signalling (MIDAS) data sets from the United Kingdom.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Interval Macroscopic Models for Traffic Networks


    Contributors:
    Gning, A (author) / Mihaylova, L (author) / Boel, R K (author)


    Publication date :

    2011-06-01


    Size :

    2026535 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Interval Macroscopic Models for Traffic Networks

    Gning, A | Online Contents | 2011



    Second-Order Macroscopic Traffic Models

    Ferrara, Antonella / Sacone, Simona / Siri, Silvia | Springer Verlag | 2018


    First-Order Macroscopic Traffic Models

    Ferrara, Antonella / Sacone, Simona / Siri, Silvia | Springer Verlag | 2018


    Distributed macroscopic traffic simulation with Open Traffic Models

    Gomes, Gabriel / Ugirumurera, Juliette / Li, Xiaoye S. | IEEE | 2020