Flow based spatio-temporal graph network model for predicting airport network delays - ENAC - École nationale de l'aviation civile Accéder directement au contenu
Communication Dans Un Congrès Année : 2024

Flow based spatio-temporal graph network model for predicting airport network delays

Résumé

With the continued advancement of the air transport industry, ensuring the safety and efficiency of air traffic operations has become a major concern. Various efforts are being made to achieve this objective, among which the prediction of delays in air traffic is of significance. Current deep learning methodologies for predicting network wide air traffic delays typically rely on historical delay data as the primary input, often neglecting the inclusion of air traffic flow data. In this paper, the FSTGMAN model is developed to explore the efficacy of incorporating traffic flow data as an additional input for predictive modeling, contrasting its performance with a model that does not utilize flow data. The findings reveal that the incorporation of flow data marginally enhances the overall accuracy of the predictions. Furthermore, the performance of our model is compared with that of baseline models such as MLP, LSTM, Transformer, and Seq2Seq, demonstrating notable advantages.
Fichier principal
Vignette du fichier
ICRAT2024_paper_62.pdf (466.24 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04636706 , version 1 (05-07-2024)

Identifiants

  • HAL Id : hal-04636706 , version 1

Citer

Zhihan Xu, Ziming Wang, Yanjun Wang, Daniel Delahaye. Flow based spatio-temporal graph network model for predicting airport network delays. International Conference on Research in Air Transportation, Eurocontrol FAA NTU, Jul 2024, Singapore, Singapore. ⟨hal-04636706⟩
0 Consultations
0 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More