Rail transit passenger flow and its distribution characteristics are markedly determined by multiple influencing factors such as the land use characteristics and the built environment. Most known studies in the relationship between traffic flow and land use tend to involve simple linear assumptions. This study introduced the real data set and analyzed the nonlinear relationship between rail transit passenger flow and its influencing factors. A numerical analysis was conducted in passenger flow collected in the rail transit stations of Shanghai. Firstly, network crawler technology was used to obtain the points of interest (POIs) for presenting land-use characteristics around subject sites. Subsequently, the gradient boosting decision tree (GBDT) model was established and extracts influencing factors and analyzes the distribution characteristics of rail transit passenger flow. The findings can provide some targeted suggestions for traffic planning and guide reasonable land use to relieve the traffic congestion caused by the over-concentrated traffic demand.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Analyzing the Influencing Factors and Distribution Characteristics of Rail Transit Passenger Flow


    Contributors:
    Zhao, Rui (author) / Wang, Wei (author) / Sun, Shichao (author) / Yu, Weijie (author)

    Conference:

    22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China


    Published in:

    CICTP 2022 ; 2463-2474


    Publication date :

    2022-09-08




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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

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


    Urban Rail Transit Passenger Flow Forecasting—XGBoost

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


    Urban Rail Transit Passenger Flow Forecasting - XGBoost

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