空中交通延误预测研究综述

doi:10.12305/j.issn.1001-506X.2022.03.19

摘要/Abstract

摘要：

空中交通系统作为典型复杂系统, 其非线性聚合的动力学特征给延误预测带来挑战, 使空中交通延误预测问题保持了开放性。近二十年来, 针对此问题, 利用传统机器学习、深度学习、复杂系统建模仿真、排队理论等, 涌现出大量研究成果。首先系统性阐述了空中交通延误产生与传播的原因, 并根据预测数值类型、预测对象以及预测时间尺度对现有研究中的延误预测类型进行分类; 然后纵贯国内外研究成果, 对空中交通延误预测方法进行分类, 详细回顾了代表性方法的实现过程, 并进行综合性对比。最后根据现有研究存在的问题, 讨论了未来可能的发展方向。

关键词: 空中交通, 延误预测, 机器学习, 建模仿真, 排队理论

Abstract:

The air traffic system is a typical complex system, and its nonlinear aggregate dynamic characteristics challenge the delay prediction, which keeps the problem of air traffic delay prediction open. In the past two decades, a large number of research results have emerged in response to this problem using traditional machine learning, deep learning, complex system modeling and simulation, and queueing theory. Firstly, the reasons for the occurrence and propagation of air traffic delays are explained systematically, and the types of delay predictions in existing research are classified according to the types of prediction values, prediction objects, and prediction time scales. Then, the relative studies at home and abroad are reviewed and the air traffic delays are analyzed. The forecasting methods are classified, the implementation process of representative methods is reviewed in detail, and a comprehensive comparison is made. Finally, based on the existing research problems, the possible future development directions is discussed.

Key words: air traffic, delay prediction, machine learning, modeling and simulation, queueing theory

中图分类号:

V355

王春政, 胡明华, 杨磊, 赵征. 空中交通延误预测研究综述[J]. 系统工程与电子技术, 2022, 44(3): 863-874.

Chunzheng WANG, Minghua HU, Lei YANG, Zheng ZHAO. Review on air traffic delay prediction[J]. Systems Engineering and Electronics, 2022, 44(3): 863-874.

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方法类型	模型	聚合/个体延误	延误输出类型	特点
经典机器学习	贝叶斯网络	聚合延误	分类型	适用于小型网络的延误分析; 面对大型复杂网络时, 工作量大; 需要经验知识
	随机森林	聚合延误/个体延误	分类型/连续型	精度较高、无需先验知识, 容易分析变量的重要度
	SVM	个体延误	分类型/连续型	易处理小样本、非线性、高维数
	多元线性回归	聚合延误	连续型	建模简单, 有助于分析各解释变量的重要程度
	BP神经网络	聚合延误	连续型	建模简单
	多元自适应回归样条	聚合延误	连续型	一定程度上能够处理延误变量的非线性关系
深度学习	LSTM-RNN	个体延误	分类型	精度良好
	MLILNN	个体延误	连续型	性能优于传统BP神经网络, 易处理名义变量
	DBN-SVR	个体延误	连续型	易处理高维数据
	CBAM-CondenseNet	个体延误	分类型	能够解决深层网络梯度消散问题, 提升网络预测精度
概率模型	平滑样条-混合分布	个体延误	概率型	延误输出结果能够包含的不确定性理解; 建模的简易性建立在对变量模糊处理的基础上

核心方法类型	工具/方法	网络要素	机场容量设置方法	是否考虑气象状况	过程时间	延误输出类型
建模仿真	NASPAC	机场(不含滑行), 进离场点, 航路扇区, 流控区	包络线方法	否	航空器性能	个体延误
	DPAT	机场(不含滑行)、航路扇区和航路点	包络线方法	否	排列论	个体延误
	Agent-Fleurquin	机场(不含滑行)	计划容量	否	常数/计划时间	个体/聚合延误
	Agent-Date Mining	机场(不含滑行)	数据挖掘	是	数据挖掘	个体/聚合延误
排队论	LMINET	机场(含滑行)、进近管制和航路管制扇区	包络线方法	否	排队论	个体/聚合延误
	LMINET2	机场(含滑行)	公布容量/包络线方法	是	计划减去缓冲时间	个体/聚合延误
	AND	机场(不含滑行)	公布容量	是	计划减去缓冲时间	个体/聚合延误

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