基于CNN和SEIR模型的航班延误扩散预测优化  

Optimization of Flight Delay Diffusion Prediction Based on CNN and SEIR Models

作　　者：朱代武[1] 蔡林均 张瀚文 ZHU Daiwu;CAI Linjun;ZHANG Hanwen(Air Traffic Management College,Civil Aviation Flight University of China,Guanghan 618307,Sichuan,China)

机构地区：[1]中国民用航空飞行学院空中交通管理学院,四川广汉618307

出　　处：《科技和产业》2025年第3期65-70,共6页Science Technology and Industry

基　　金：民航局安全能力建设项目(14002600100015J013)。

摘　　要：大面积航班延误引起的延误扩散现象对空中交通网络有显著影响。为更好地预测和控制延误扩散,针对航班延误扩散的非线性复杂特性以及实时性、准确性难以兼顾的特点,提出结合卷积神经网络(CNN)和传统流行病SEIR(易感-潜伏-感染-恢复)模型的延误扩散预测模型。基于航班延误扩散传播机理和SEIR模型建立航班延误扩散动力学模型,通过卷积神经网络对模型中的关键参数进行优化。利用MATLAB对优化后模型进行仿真。研究发现结合卷积神经网络模型后相比传统SEIR模型准确率提升了17.95%。The delay diffusion phenomenon caused by large-scale flight delays has a significant impact on air traffic networks.In order to better predict and control delay diffusion,a delay diffusion prediction model combining convolutional neural network(CNN)and traditional epidemiological SEIR(susceptible-exposed-infectious-recovered)model is proposed for the nonlinear and complex characteristics of flight delay diffusion,as well as for the difficulty of combining real-time and accuracy.Based on the flight delay diffusion propagation mechanism and SEIR model,a flight delay diffusion dynamics model was established,and the key parameters in the model are optimized by convolutional neural network.The optimized model was simulated using MATLAB,and the accuracy was improved by 17.95%compared with the traditional SEIR model after combining the CNN model.

关 键 词：航班运行 航班延误扩散 航班链 SEIR(易感-潜伏-感染-恢复)模型 卷积神经网络(CNN) 

分 类 号：U8[交通运输工程]