Effective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction accuracy. To address these shortcomings, this study proposes the Impedance-Spatio-Temporal Topological Network (Impedance-STTN) prediction model. The Impedance-STTN model integrates K-medoids clustering for data analysis, generating a real-time impedance matrix from impedance functions, traffic big data, and real-time flow data. This approach captures dynamic node relationships within the spatio-temporal network, enhancing prediction accuracy. Experimental results demonstrate the superior predictive performance of the Impedance-STTN model, achieving accuracies of 94.79%, 93.78%, and 93.11% in 5 min, 15 min, and 30 min predictions, respectively. These results outperform existing models, especially in long-term predictions. The findings underscore the model's high accuracy and effectiveness across varying prediction durations, marking a significant advancement in traffic flow prediction. This suggests promising avenues for future research and practical applications.


    Access

    Download


    Export, share and cite



    Title :

    An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix


    Contributors:
    Wenhao Li (author) / Yanyan Chen (author) / Yuyan Pan (author) / Yunchao Zhang (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Improved space-time network traffic flow prediction method based on impedance matrix

    LI WENHAO / CHEN YANYAN / ZHANG YUNCHAO et al. | European Patent Office | 2023

    Free access

    Global spatio‐temporal dynamic capturing network‐based traffic flow prediction

    Haoran Sun / Yanling Wei / Xueliang Huang et al. | DOAJ | 2023

    Free access

    Global spatio‐temporal dynamic capturing network‐based traffic flow prediction

    Sun, Haoran / Wei, Yanling / Huang, Xueliang et al. | Wiley | 2023

    Free access

    Spatio-Temporal AutoEncoder for Traffic Flow Prediction

    Liu, Mingzhe / Zhu, Tongyu / Ye, Junchen et al. | IEEE | 2023


    Traffic flow prediction method for traffic flow spatio-temporal data information

    LIU PENG / CHU YUQUAN | European Patent Office | 2024

    Free access