Flight Delay
Flight delays represent a significant challenge in air travel, impacting airlines, passengers, and the broader economy. Current research focuses on improving the accuracy and timeliness of delay prediction using machine learning, particularly employing advanced architectures like Temporal Fusion Transformers, LSTM networks, and hybrid models combining deep learning with classical techniques. These models analyze diverse factors such as weather, airport capacity, air traffic management strategies (including Ground Delay Programs), and airline operations to predict delays with greater precision, aiming to optimize resource allocation and improve operational efficiency. The ultimate goal is to develop more effective strategies for mitigating delays and enhancing the overall predictability and reliability of air travel.
Papers
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments
Ke Liu, Fan Hu, Hui Lin, Xi Cheng, Jianan Chen, Jilin Song, Siyuan Feng, Gaofeng Su, Chen Zhu
Airport Delay Prediction with Temporal Fusion Transformers
Ke Liu, Kaijing Ding, Xi Cheng, Guanhao Xu, Xin Hu, Tong Liu, Siyuan Feng, Binze Cai, Jianan Chen, Hui Lin, Jilin Song, Chen Zhu