报告题目：Flight delay prediction with priority information of weather and non-weather features

报告所属学科：管理科学与工程

报告人：董智捷Zhijie (Sasha) Dong（美国休斯敦大学）

报告时间：2023年9月1日 15:00-18:30

报告地点：经管学院704会议室

报告摘要：

Flight delay prediction is a major topic in intelligent airport management systems, which emphasizes the use of historical data and potential features to estimate whether a future flight will delay. However, many factors affect flight delays, and these factors can be categorized into weather features (e.g., temperature, humidity, and wind speed) and non-weather features (day-of-month, day-of-week, scheduled departure and arrival time). Moreover, the impacts of weather and non-weather factors on flight delays are different. Weather features play a more important role in adverse weather conditions and are the main reason for long flight delays. When the weather condition changes from severe to non-severe, non-weather features are the main reason for flight delays, and the caused delays are relatively short. Such different impacts on flight delays raise a strong need for considering the priority information of weather and non-weather features in flight delay prediction. In this paper, we design a variant of the Random Forest model to consider the priority information of weather and non-weather features to predict flight delays. A clustering algorithm-based analysis approach is developed to assess the impact of weather and non-weather features on flight delays and draw conclusions on the priority information of weather and non-weather features. A probability sampling method is embedded in the Random Forest at the feature selection stage to perform a prior choice for weather and non-weather features to help select the key influential features. Experiments were carried out on U.S. domestic flights in July 2018, and the comparison results demonstrate that the proposed model can significantly increase flight delay prediction accuracy.

报告人简介：

董智捷Zhijie (Sasha) Dong，博士，现任美国休斯敦大学建筑管理系副教授。博士、硕士、本科分别就读于美国康奈尔大学、哥伦比亚大学和南京大学。回归学术界前，曾就职于联邦快递、CSX运输和通用汽车等国际知名企业。主要研究方向：通过优化和人工智能提高复杂系统的效率(如供应链和运输系统)。研究获得美国自然科学基金(NSF)、美国联邦公路管理局(FHWA)、美国能源部(DOE)等机构与美国超威半导体(AMD)、壳牌集团(Shell)等企业的支持。本人荣获美国自然科学基金迷你职业奖、OSSEEER早期职业研究员奖、INFORMS MIF早期职业奖等国际奖项。同时任职于多个国际学术协会和组织，包括担任Nature子刊Communications Engineering编委，工业和系统工程师协会(IISE)高级会员和物流与供应链分部的候选主席。