Accurately predicting origin-destination (OD) passenger flows serves as the basis for implementing efficient plans, including line planning and timetabling. However, due to the complexity and variety of OD passenger flows types, general prediction models have difficulty in capturing the features of different OD passenger flows, which in turn leads to poor prediction performance. To address this issue, we propose an integrated framework that combines clustering and prediction methods. First, an unsupervised deep learning model is devised to automatically cluster OD flow types by capturing shape characteristics. Second, three types of features are created to enhance training efficiency, including static features, time-dependent observed features, and time-dependent known features. Based on the clustering of OD passenger flow, a weighted adaptive passenger flow prediction model is developed. The study employs a temporal fusion transformers model to enable multitype OD passenger flow prediction. In the numerical experiments, the model was applied to the urban rail transit in South China, and the model clustered 15,168 OD pairs into 4 types for prediction. The findings show that this approach enhanced the prediction accuracy by 2.0%–9.6% compared to the LSTM model and by 1.6%–4.3% compared to the Graph WaveNet. Moreover, the model can accurately assess the various features for diverse types of OD flows.


    Access

    Download


    Export, share and cite



    Title :

    Multitype Origin-Destination (OD) Passenger Flow Prediction for Urban Rail Transit: A Deep Learning Clustering First Predicting Second Integrated Framework


    Contributors:
    Zhaocha Huang (author) / Han Zheng (author) / Kuan Yang (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    Integrated Prediction Model for Urban Rail Transit Station Feeder Passenger Flow

    Junchen DAI / Ping LI / Ying CUI et al. | DOAJ | 2024

    Free access

    Urban rail transit passenger flow prediction method under emergency

    ZHANG WENQIANG / LIU YURAN / ZHANG HANXIAO et al. | European Patent Office | 2023

    Free access

    Urban Rail Transit Passenger Flow Forecasting - XGBoost

    Sun, Xiaoli / Zhu, Caihua / Ma, Chaoqun | TIBKAT | 2022


    Urban Rail Transit Passenger Flow Forecasting—XGBoost

    Sun, Xiaoli / Zhu, Caihua / Ma, Chaoqun | ASCE | 2022