In order to provide a scientific basis for the resource allocation in the stage of checked baggage, improve the service efficiency of airport passenger terminal. According to the flight data of an international airport passenger terminal in 2012 May, this paper establish the BP artificial neural network and multiple regression prediction models respectively, in which the influencing factors are decided by grey relationship weight analysis. A set of comparative predictions were done by analyzing three types of data, namely all flight data, single flight data and data of flights with the same destination respectively. The results show that the prediction effect is better when using the last type of data as the sample data and the result of multiple regression model is superior to the BP neural network. It will have great practical significance in the actual source allocation in the stage of checked baggage.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Comparative study on forecasting method of departure flight baggage demand


    Contributors:
    Cheng, Shaowu (author) / Gao, Qian (author) / Zhang, Yapping (author)


    Publication date :

    2014-08-01


    Size :

    144553 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    BAGGAGE FASTENING DEVICE AND FLIGHT VEHICLE

    NEMOTO TAKUYA / OGATA DAIKI | European Patent Office | 2022

    Free access

    BAGGAGE FASTENING DEVICE AND FLIGHT VEHICLE

    NEMOTO TAKUYA / OGATA DAIKI | European Patent Office | 2022

    Free access


    Flight Demand Forecasting with Transformers

    Wang, Liya / Mykityshyn, Amy / Johnson, Craig et al. | AIAA | 2022