In modern air transportation, the direct share is the ratio of direct passengers to total passengers on a directional origin and destination (O&D) pair. The forecasting of direct share time series on the O&D level, as part of the detailed demand forecasting, plays a fundamental role in air transportation planning and development. An accurate forecasting of the O&D direct share time series can benefit the air transportation planners, airlines, and airports in multiple ways. Based on the previous analysis, the direct share time series is O&D specific. This research focuses on developing accurate direct share time series forecasting models on O&D markets with different characteristics. Both classical time series models and supervised learning regression models are investigated carefully. A novel hybrid model that combines time series concept and machine learning modeling techniques is proposed, which can provide more accurate forecasting performance and valuable insights into the O&D markets. To automatically select the forecasting model for each O&D pair, a general modeling framework is proposed for direct share time series forecasting. Based on the forecasting performance comparison, the modeling framework can provide promising direct share time series forecasting, which is a reliable replacement for the model used in the Federal Aviation Administration Terminal Area Forecast.


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    Title :

    Air Transportation Direct Share Time Series Forecasting: A Hybrid Model


    Contributors:
    Zheng, Xufang (author) / Wei, Peng (author)

    Published in:

    Publication date :

    2020-12-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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