Accurate flight delay prediction is crucial for the secure and effective operation of the air traffic system. Recent advances in modeling inter-airport relationships present a promising approach for investigating flight delay prediction from the multi-airport scenario. However, the previous prediction works only accounted for the simplistic relationships such as traffic flow or geographical distance, overlooking the intricate interactions among airports and thus proving inadequate. In this paper, we leverage casual inference to precisely model inter-airport relationships and propose a self-corrective spatio-temporal graph neural network (named CausalNet) for flight delay prediction. Specifically, Granger causality inference coupled with a self-correction module is designed to construct causality graphs among airports and dynamically modify them based on the current airport’s delays. Additionally, the features of the causality graphs are adaptively extracted and utilized to address the heterogeneity of airports. Extensive experiments are conducted on the real data of top-74 busiest airports in China. The results show that CausalNet is superior to baselines. Ablation studies emphasize the power of the proposed self-correction causality graph and the graph feature extraction module. All of these prove the effectiveness of the proposed methodology.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Spatio-Temporal Approach With Self-Corrective Causal Inference for Flight Delay Prediction


    Beteiligte:
    Zhu, Qihui (Autor:in) / Chen, Shenwen (Autor:in) / Guo, Tong (Autor:in) / Lv, Yisheng (Autor:in) / Du, Wenbo (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    11636415 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic network abnormal situation spatio-temporal evolution method fusing causal inference

    MA XIAOLEI / LI TIANQI / TAN ERLONG et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

    Shao, Wei / Prabowo, Arian / Zhao, Sichen et al. | ArXiv | 2021

    Freier Zugriff

    Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields

    Lindeberg, T. | British Library Online Contents | 2016



    Granger Causal Inference for Interpretable Traffic Prediction

    Zhang, Lei / Fu, Kaiqun / Ji, Taoran et al. | IEEE | 2022