This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic. The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams. We argue that these methods are not suitable to generate the percentile-based stochastic fundamental diagrams, because the results generated by least-squares methods represent weighted sample mean, rather than percentile. The second shortcoming is widespread use of independent modeling methodology for a family of percentile-based fundamental diagrams. Existing methods are inadequate to coordinate the fundamental diagrams in the same family, and consequently, are not in alignment with the basic rules in probability theory and statistics. To address these issues, this paper proposes a holistic modeling framework based on the concept of mean absolute error minimization. The established model is convex, but non-differentiable. To efficiently implement the proposed methodology, we further reformulate this model as a linear programming problem which could be solved by the state-of-the-art solvers. Experimental results using real-world traffic flow data validate the proposed method.


    Access

    Download


    Export, share and cite



    Title :

    Model on empirically calibrating stochastic traffic flow fundamental diagram


    Contributors:
    Shuaian Wang (author) / Xinyuan Chen (author) / Xiaobo Qu (author)


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Integrated Multi Regime and Gaussian Processes Model for Calibrating Traffic Fundamental Diagram

    Kidando, Emmanuel / Balyagati, Philip / Ngereza, Abdul et al. | IEEE | 2024


    Fundamental Diagram of Traffic Flow

    Li, Jia / Zhang, H. Michael | Transportation Research Record | 2011


    A traffic fundamental diagram calibrating methodology to avoid unbalanced speed–density observations

    Chunbo Zhang / Zhaoguo Huang / Yonggang Wang | DOAJ | 2021

    Free access


    One More Fundamental Diagram of Traffic Flow

    Mahnke, R. / Kaupuzs, J. | British Library Conference Proceedings | 1998