Anticipating uncertainty in short-term traffic flow is crucial for effective traffic management within intelligent transportation systems. Various methods for predicting uncertainty have been proposed and implemented. However, conventional techniques struggle to provide accurate forecasts when confronted with sparse data. Hence, this study focuses on developing an uncertainty prediction model for short-term traffic flow under limited data conditions. A novel grey model that considers the volatility of the traffic data is proposed, which extends the grey model (GM) by integrating two techniques: smooth pre-processing and background value construction. The performance of the proposed novel grey model is mainly illustrated by comparing the novel grey model with the traditional GM model. Our results, in terms of uncertainty quantification, demonstrate that the proposed model outperforms the GM model regarding mean kick-off percentage (KP), width interval (WI) and width amplitude.


    Access

    Download


    Export, share and cite



    Title :

    Short-Term Traffic Flow Uncertainty Prediction Based on Novel GM(1,1)


    Contributors:
    Xu Dong CAO (author) / Qin SHI (author) / Yi Kai CHEN (author) / Chen Chen CHEN (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Traffic flow short-term prediction method

    XIAO HONGBO / XIAO JIANHUA / PENG XIAONING et al. | European Patent Office | 2024

    Free access

    Short-Term Traffic Flow Uncertainty Prediction Based on Deep Kernel Adaptive Interval Grey Model

    Wang, Changyue / Chen, Huifen / Yang, Qiyuan et al. | IEEE | 2025


    Event-Based Short-Term Traffic Flow Prediction Model

    Head, K. L. / National Research Council / Transportation Research Board | British Library Conference Proceedings | 1995


    Event-Based Short-Term Traffic Flow Prediction Model

    Head, K.Larry | Online Contents | 1995