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A New Way of Airline Traffic Prediction Based on GCN-LSTM

With the development of society and the improvement of people's material level, more and more people like to travel by airplane. If we can predict the passenger flow of an airline in advance, it can be used as an important decision-making basis for its flight route planning, crew scheduling pla...

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Detalles Bibliográficos
Autor principal: Yu, Jiangni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702519/
https://www.ncbi.nlm.nih.gov/pubmed/34955800
http://dx.doi.org/10.3389/fnbot.2021.661037
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author Yu, Jiangni
author_facet Yu, Jiangni
author_sort Yu, Jiangni
collection PubMed
description With the development of society and the improvement of people's material level, more and more people like to travel by airplane. If we can predict the passenger flow of an airline in advance, it can be used as an important decision-making basis for its flight route planning, crew scheduling planning and ticket price formulation in the process of management and operation. However, due to the high complexity of aviation network, the existing traffic prediction methods generally have the problem of low prediction accuracy. In order to overcome this problem, this paper makes full use of graph convolutional neural network and long—short memory network to construct a prediction system with short—term prediction ability. Specifically, this paper uses the graph convolutional neural network as a feature extraction tool to extract the key features of air traffic data, and solves the problem of long term and short term dependence between data through the long term memory network, then we build a high-precision air traffic prediction system based on it. Finally, we design a comparison experiment to compare the algorithm with the traditional algorithms. The results show that the algorithm we proposed in this paper has a higher accuracy in air flow prediction according to the lower loss function value.
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spelling pubmed-87025192021-12-25 A New Way of Airline Traffic Prediction Based on GCN-LSTM Yu, Jiangni Front Neurorobot Neuroscience With the development of society and the improvement of people's material level, more and more people like to travel by airplane. If we can predict the passenger flow of an airline in advance, it can be used as an important decision-making basis for its flight route planning, crew scheduling planning and ticket price formulation in the process of management and operation. However, due to the high complexity of aviation network, the existing traffic prediction methods generally have the problem of low prediction accuracy. In order to overcome this problem, this paper makes full use of graph convolutional neural network and long—short memory network to construct a prediction system with short—term prediction ability. Specifically, this paper uses the graph convolutional neural network as a feature extraction tool to extract the key features of air traffic data, and solves the problem of long term and short term dependence between data through the long term memory network, then we build a high-precision air traffic prediction system based on it. Finally, we design a comparison experiment to compare the algorithm with the traditional algorithms. The results show that the algorithm we proposed in this paper has a higher accuracy in air flow prediction according to the lower loss function value. Frontiers Media S.A. 2021-12-10 /pmc/articles/PMC8702519/ /pubmed/34955800 http://dx.doi.org/10.3389/fnbot.2021.661037 Text en Copyright © 2021 Yu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Yu, Jiangni
A New Way of Airline Traffic Prediction Based on GCN-LSTM
title A New Way of Airline Traffic Prediction Based on GCN-LSTM
title_full A New Way of Airline Traffic Prediction Based on GCN-LSTM
title_fullStr A New Way of Airline Traffic Prediction Based on GCN-LSTM
title_full_unstemmed A New Way of Airline Traffic Prediction Based on GCN-LSTM
title_short A New Way of Airline Traffic Prediction Based on GCN-LSTM
title_sort new way of airline traffic prediction based on gcn-lstm
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702519/
https://www.ncbi.nlm.nih.gov/pubmed/34955800
http://dx.doi.org/10.3389/fnbot.2021.661037
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