Flight Delay Propagation Prediction Model Based on CBAM-CondenseNet
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摘要:
针对航班延误衍生的航班延误波及问题，该文提出一种基于CBAM-CondenseNet的航班延误波及预测模型。首先，通过分析航班延误在航空网络内产生的延误波及现象，确定会受前序延误航班影响的航班链；其次，对选定的航班链数据进行清洗，将航班信息与机场信息进行数据融合；最后，提出改进的CBAM-CondenseNet算法对融合后的数据进行特征提取，构建Softmax分类器对首班离港航班延误波及的后续离港航班延误等级进行预测。该文提出的CBAM-CondenseNet算法融合了CondenseNet和CBAM的优势，采用通道和空间注意力机制来加强网络结构深层信息的传递。实验结果表明，算法改进后有效提升网络性能，预测准确率可达97.55%。
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关键词:
- 航班延误波及预测 /
- CBAM-CondenseNet /
- 数据融合 /
- 注意力机制
Abstract:For the problem of flight delay propagation caused by flight delay, a flight delay wave prediction model based on CBAM-CondenseNet is presented. Firstly, by analyzing the delays propagation in the aviation network caused by flight delays, the flight chain affected by the pre-order delays is determined; Secondly, the selected flight chain data is cleaned and the flight information and airport information are fused; Finally, an improved CBAM-CondenseNet algorithm is proposed to extract the number of fused flights. According to feature extraction, a Softmax classifier is constructed to predict the delays of the first departure flights and the subsequent flights. The CBAM-CondenseNet algorithm proposed in this paper combines the advantages of CondenseNet and CBAM, and uses channel and spatial attention mechanism to enhance the transmission of deep information in network structure. The experimental results show that the improved algorithm can effectively improve the network performance, and the prediction accuracy can reach 97.55%.
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表 1 航班延误等级划分
延误等级 延误时间 未延误 $T \leq 15$ 轻度延误 $15 < T \leq 60$ 中度延误 $60 < T \leq 120$ 高度延误 $120 < T \leq 240$ 重度延误 $T > 240$ 
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表 2 算法改进前后FLOPs(M)
网络层数 CondenseNet CBAM-CondenseNet 18 20.55 20.77 36 165.45 165.94 44 218.27 218.87
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表 4 分类准确率对比(%)
网络层数 CondenseNet CBAM-CondenseNet 18 93.77 94.19 28 95.96 96.96 36 96.52 97.45 44 96.66 97.55 70 96.56 97.56 102 96.66 97.55 126 96.75 97.55
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表 5 不同算法模型分类准确率对比(%)
网络
层数DenseNet SE-
DenseNetCondenseNet CBAM-
CondenseNet18 91.86 92.33 93.77 94.19 36 92.28 92.80 96.52 97.45 44 92.57 93.14 96.66 97.55
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