基于团簇随机连接的CliqueNet航班延误预测模型

针对目前民航运输业延误率较高，而传统算法难以解决高精度延误预测的问题，提出一种基于随机连接团簇网络（CliqueNet）航班延误预测模型。该模型首先对航班数据和相关气象数据进行融合；然后，充分利用改进后的网络模型对融合后的数据集进行特征提取；最后，使用Softmax分类器进行航班离港延误各等级的高精度预测。模型的主要特点是：在团簇特征层的随机连接，以及在转换层引入通道和空间注意力残差（CSAR）模块。前者以更为有效的连接方式传递特征信息；后者则对特征信息进行通道和空间维度的双重标定，以提高准确率。实验结果表明，对融合数据进行预测，引入随机连接和CSAR模块后，新模型的准确率分别提高了0.5%、1.3%，最终准确率能达到93.40%。

CliqueNet flight delay prediction model based on clique random connection

Aiming at the current high delay rate of the civil aviation transportation industry, and the fact that the high-precision delay prediction problem can hardly be solved by traditional algorithms, a randomly connected Clique Network (CliqueNet) based flight delay prediction model was proposed. Firstly, the flight data and related weather data were fused by the model. Then, making full use of the improved network model to extract features from the fused dataset. Finally, the softmax classifier was used to predict the flight departure delay of all levels with high precision. The main features of the model include random connection of clique feature layers and the introduction of Channel-wise and Spatial Attention Residual (CSAR) block to the transition layer. The former transmits the feature information in a more effective connection; and the latter double-calibrates the feature information on the channel and spatial dimensions to improve accuracy. Experimental results show that the prediction accuracy of the fused data is improved by 0.5% and 1.3% respectively with the introduction of random connection and CSAR block, and the final accuracy of the new model reaches 93.40%.