Passenger travel flows of urban rail transit during holidays usually show distinct characteristics different from normal days. To ensure efficient operation management, it is essential to accurately predict the distribution of holiday passenger flow. Based on Automatic Fare Collection (AFC) data, this paper explores the passengers’ destination choice differences between normal days and holidays, as well as one-way tickets and public transportation cards, which provides support for variable selection in modeling. Then, a forecasting model of holiday travel distribution is proposed, in which the destination choice model is established for representing local and nonlocal passengers. Meanwhile, explanatory variables such as land matching degree, scenic spot dummy, and level of service variables are introduced to deal with the particularity of holiday passengers’ travel behavior. The parameters calibrated by the improved weighted exogenous sampling maximum likelihood (WESML) method are applied to predict passenger flow distribution in different holiday cases with annual changes in the metro network, using the data collected from Guangzhou Metro, China. The results show that the proposed model is valid and performs better than the other comparable models in terms of forecasting accuracy. The proposed model has the capability to provide a more universal and accurate passenger flow distribution prediction method for urban rail transit in different holiday scenarios with network changes.


    Access

    Download


    Export, share and cite



    Title :

    Forecasting Passenger Flow Distribution on Holidays for Urban Rail Transit Based on Destination Choice Behavior Analysis


    Contributors:
    Enjian Yao (author) / Junyi Hong (author) / Long Pan (author) / Binbin Li (author) / Yang Yang (author) / Dongbo Guo (author)


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    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


    Urban rail transit passenger flow forecasting based on prophet-GRU combined model

    Lu, Yongjiu / Ye, Mao / Zhang, Renjie et al. | SPIE | 2023