With the accelerated development of cities, the traffic capacity cannot catch up with traffic rising. The urban rail transit system is facing severe challenges. Accurate prediction of passenger flow can help optimize the operation plan and improve operation efficiency. Traditional machine learning-based intelligent control methods are restricted by insufficient data. Owing to lacking effective incentives and trust, data from different urban rail lines or operators cannot be shared directly. In this paper, we propose a distributed federal learning method for accurate prediction of rail transit passenger flow based on blockchain. The proposed method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federal learning. Considering the limitations of the traditional time series model, we choose the distributed long and short term memory (LSTM) networks as the supervised learning model for passenger flow prediction. In addition, we establish an incentive mechanism to reward those participants who contribute to the model. The simulation results demonstrate high efficiency and accuracy of our proposed intelligent control method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Blockchain Based Federal Learning Method for Urban Rail Passenger Flow Prediction


    Beteiligte:
    Shen, Chunzi (Autor:in) / Zhu, Li (Autor:in) / Hua, Gaofeng (Autor:in) / Zhou, Linyan (Autor:in) / Zhang, Lin (Autor:in)


    Erscheinungsdatum :

    20.09.2020


    Format / Umfang :

    355027 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Urban rail transit passenger flow prediction method under emergency

    ZHANG WENQIANG / LIU YURAN / ZHANG HANXIAO et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    OD prediction of urban rail transit passenger flow based on passenger flow trend characteristics

    Wang, Yubian / Liu, Xiang / Alexandrovich, Erofeev Alexander | SPIE | 2023



    Short-Term Passenger Flow Prediction for Urban Rail Based on Improved EEMD-Ensemble Learning

    Qiao, Yaoqin / Zhou, Huijuan / Zhang, Xiayu et al. | Springer Verlag | 2024