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.

在现代航空运输中，直接份额是指有方向的始发地和目的地（O＆D）对上的直接乘客与总乘客的比率。作为详细需求预测的一部分，在O＆D级别上直接共享时间序列的预测在航空运输计划和开发中起着根本性的作用。运维直接共享时间序列的准确预测可以使航空运输计划者，航空公司和机场从多种方面受益。根据先前的分析，直接共享时间序列特定于O＆D。本研究的重点是在具有不同特征的O＆D市场上开发准确的直接份额时间序列预测模型。仔细研究了经典时间序列模型和监督学习回归模型。提出了一种结合时间序列概念和机器学习建模技术的新型混合模型，该模型可以提供更准确的预测性能和对O＆D市场的宝贵见解。为了自动为每个运维对选择预测模型，提出了用于直接共享时间序列预测的通用建模框架。基于预测性能的比较，建模框架可以提供有希望的直接共享时间序列预测，这是联邦航空局航站楼面积预测中使用的模型的可靠替代。提出了用于直接共享时间序列预测的通用建模框架。基于预测性能的比较，建模框架可以提供有希望的直接共享时间序列预测，这是联邦航空局航站楼面积预测中使用的模型的可靠替代。提出了用于直接共享时间序列预测的通用建模框架。基于预测性能的比较，建模框架可以提供有希望的直接共享时间序列预测，这是联邦航空局航站楼面积预测中使用的模型的可靠替代。