Tanaya Chaudhuri, Sheng Zhang, Yicheng Zhang. Attention-based Deep Learning Model for Flight Delay Prediction using Real-time Trajectory. SESAR Innovation Days Conference 2024, 2024-006 (2024). href='https://oar.a-star.edu.sg/dashboard/my-submissions/view/21244'
Abstract:
This paper presents a deep learning model termed LSTM-Attention based Time-dependent Flight-delay Classifier (LATTICE) for real-time flight arrival delay classification. Initially, this model incorporates a comprehensive set of factors influencing flight delays, including weather conditions, flight information, and en-route real-time trajectory data provided by ADS-B technology. Subsequently, LATTICE leverages a full sequenced LSTM network for the extraction of deep temporal trajectory features and employs an attention network for the allocation of weights and mapping of relevant information. Ultimately, the model utilizes a masking layer to address the challenges posed by varying trajectory lengths, and experimental results demonstrate a significant enhancement in the accuracy of flight delay predictions as a result of these integrated measures. The model classifies incoming flights into On-Time/Late and Early/Punctual/Late. On being evaluated against historical data, it achieves about 91% accuracy and 0.96 AUC at predicting delay, yielding better predictions compared to baseline models. Trajectory inputs improve the prediction by about 15%. The model is real-time via ADS-B technology, robust via adaptive improvement with continuous training, and able to handle both late and early arrivals. This paper demonstrates that the real-time trajectory inferred from ADS-B messages can add significantly to the reliability of delay prediction.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation (NRF), Singapore - Aviation Transformation Programme
Grant Reference no. : EC-2021-058