关键词: 航班延误预测, 双通道卷积神经网络, 数据融合, 直通通道, 卷积衰减因子

Abstract: Nowadays, flight delay prediction has a large amount of data and the feature extraction is difficult. Traditional models can not solve these problems effectively, so a flight delay prediction model based on Dual-Channel Convolutional Neural Network (DCNN) was proposed. Firstly, flight data and meteorological data were fused in the model. Then, a DCNN was used to extract features automatically, and Batch Normalization (BN) and Padding strategy were used to improve the classification prediction performance of arrival delay level. Secondly, to guarantee the lossless transmission of feature matrix and enhance the patency of deep network, a straight channel was used in the Convolutional Neural Network (CNN). Meanwhile, convolution attenuation factor was introduced to control the sparseness of feature matrix, it also was used to control the proportion of feature matrix from different depth and guarantee the stability of the model. The experimental results indicate that the proposed model has a stronger data processing capability than the traditional model, and through fusion of meteorological data, the accuracy of the proposed model is improved 1 percentage point. When the networks are deepened, the model can guarantee the stability of gradients and train the deeper network, thus improves the accuracy to 92.1%. The proposed model based on DCNN algorithm has sufficient feature extraction and better prediction performance than the contrast model, it can better serve the civil aviation decision-making.

Key words: flight delay prediction, Dual-Channel Convolutional Neural Network (DCNN), data fusion, straight channel, convolution attenuation factor