Tracing delay network in air transportation combining causal propagation and complex network

DaoZhong Feng , Bin Hao , JiaJian Lai
{"title":"Tracing delay network in air transportation combining causal propagation and complex network","authors":"DaoZhong Feng ,&nbsp;Bin Hao ,&nbsp;JiaJian Lai","doi":"10.1016/j.ijin.2024.01.006","DOIUrl":null,"url":null,"abstract":"<div><p>In air transportation, monitoring delays and making informed decisions at a system level is crucial for network managers. Causal selection methods have recently witnessed increased adoption for the analysis of multi-observations. Systematic Path Isolation (SPI) stands out as an effective mechanism for selecting causal pathways in time-series data. However, specific improvements are needed to ensure the effectiveness within the aviation system. This paper proposes an SPI-based causal inference method that incorporates the Granger test and the Kernel-based test, accommodating both linear and non-linear relationships, thereby enabling better condition selection. Additionally, the two-step SPI test employs the Kernel-based Conditional Independence test due to its suitability for handling complex data with nonlinear relationships, and it avoids explicit feature extraction. Validation of delay tracing involves the use of complex network metrics and a specially designed load-embedded metric for identifying daily states. The case study results demonstrate the effectiveness of the network generated by the proposed method in accurately tracing dynamic states, particularly through the proposed indicator. In static propagation detection, network motifs can serve as micro-expressions, particularly with convergence and transmission forms during high delays. This research contributes to refine the depiction of delay propagation in the air transport network, enhancing the ability to trace delay trends in dynamic and static perspectives.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 63-76"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266660302400006X/pdfft?md5=9f19ef81d4c4274e179acb334e890fc8&pid=1-s2.0-S266660302400006X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Networks","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266660302400006X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In air transportation, monitoring delays and making informed decisions at a system level is crucial for network managers. Causal selection methods have recently witnessed increased adoption for the analysis of multi-observations. Systematic Path Isolation (SPI) stands out as an effective mechanism for selecting causal pathways in time-series data. However, specific improvements are needed to ensure the effectiveness within the aviation system. This paper proposes an SPI-based causal inference method that incorporates the Granger test and the Kernel-based test, accommodating both linear and non-linear relationships, thereby enabling better condition selection. Additionally, the two-step SPI test employs the Kernel-based Conditional Independence test due to its suitability for handling complex data with nonlinear relationships, and it avoids explicit feature extraction. Validation of delay tracing involves the use of complex network metrics and a specially designed load-embedded metric for identifying daily states. The case study results demonstrate the effectiveness of the network generated by the proposed method in accurately tracing dynamic states, particularly through the proposed indicator. In static propagation detection, network motifs can serve as micro-expressions, particularly with convergence and transmission forms during high delays. This research contributes to refine the depiction of delay propagation in the air transport network, enhancing the ability to trace delay trends in dynamic and static perspectives.

结合因果传播和复杂网络追踪航空运输中的延误网络
在航空运输中,监控延误并在系统层面做出明智决策对网络管理人员至关重要。最近,越来越多的人采用因果选择方法来分析多重观测数据。系统路径隔离(SPI)是在时间序列数据中选择因果路径的有效机制。然而,要确保其在航空系统中的有效性,还需要进行具体的改进。本文提出了一种基于 SPI 的因果推断方法,该方法结合了格兰杰检验和基于核的检验,同时兼顾线性和非线性关系,从而实现更好的条件选择。此外,由于基于核的条件独立性检验适合处理具有非线性关系的复杂数据,因此两步 SPI 检验采用了基于核的条件独立性检验,并避免了明确的特征提取。延迟跟踪的验证包括使用复杂的网络指标和专门设计的负载嵌入指标来识别日常状态。案例研究结果表明,所提方法生成的网络在准确追踪动态状态方面非常有效,尤其是通过所提指标。在静态传播检测中,网络图案可以作为微观表达,特别是在高延迟时的收敛和传输形式。这项研究有助于完善对航空运输网络中延误传播的描述,提高从动态和静态角度追踪延误趋势的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
12.00
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信