A New Way of Airline Traffic Prediction Based on GCN-LSTM - Yu, Jiangni (Frontiers in Neurorobotics, 2021)
With the development of society and the improvement of people's material level, airplanes are becoming more and more a means of transportation for people to travel. If an airline can predict the passenger flow 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, and builds a high-precision air traffic prediction system based on it. Finally, we design a comparison experiment to compare the algorithm with the traditional algorithm. The comparison results show that the algorithm has obvious advantages in air flow prediction.