A geographical and operational deep graph convolutional approach for flight delay prediction |
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Institution: | 1. School of Electronic and Information Engineering, Beihang University, Beijing 100191, China;2. Key Laboratory of National Airspace Technology, Beijing 100085, China;3. National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;4. Research Institute for Frontier Science, Beihang University, Beijing 100191, China |
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Abstract: | Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement. |
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Keywords: | Flight delay prediction Flight operation pattern Geographical interactive information Graph neural network Spatial-temporal information |
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