Delay Prediction
Delay prediction research aims to accurately forecast delays across various systems, from air travel and railways to court proceedings, improving operational efficiency and resource allocation. Current research emphasizes the use of machine learning, particularly deep learning architectures like recurrent neural networks (RNNs, including LSTMs and GRUs), transformers, and decision forests, to model complex temporal dependencies and incorporate diverse input features. These advancements improve prediction accuracy and offer insights into the key factors driving delays, leading to better decision-making and potentially mitigating negative impacts across numerous sectors.
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