模拟 COVID-19 对美国、欧洲国家和中国航空客运交通的影响

IF 3.9 2区 工程技术 Q2 TRANSPORTATION Journal of Air Transport Management Pub Date : 2024-02-10 DOI:10.1016/j.jairtraman.2024.102556
Wai Ming To , Peter K.C. Lee
{"title":"模拟 COVID-19 对美国、欧洲国家和中国航空客运交通的影响","authors":"Wai Ming To ,&nbsp;Peter K.C. Lee","doi":"10.1016/j.jairtraman.2024.102556","DOIUrl":null,"url":null,"abstract":"<div><p>The COVID-19 pandemic has changed many aspects of people's lives including travel since early 2020. Specifically, it has adversely affected people traveling by air and has hit the air transport industry significantly. But, how big is the COVID-19 impact? In order to answer such a question, we collected air passenger traffic data from the US, European countries, and China which accounted for over 75% of the world's total air passenger traffic. Air passenger traffic data in these three regions during the period January 2010 to December 2019 were modeled using seasonal autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models were used to predict air passenger traffic from January 2011 to December 2019 (just before the spread of COVID-19) and the accuracy of the models was evaluated. The models were then used to predict air passenger traffic from January 2020 to December 2022 for the case without COVID-19. The COVID-19 impacts on air passenger traffic were estimated by calculating the differences in predicted and actual air passenger numbers in monthly basis. Results showed that air passenger traffic was significantly recovered in the US and European countries but it encountered significant falls in 2021 and 2022 in China due to spikes in COVID-19 variant cases in many provinces and the implementation of zero-tolerance COVID-19 policy. Implications of the study are given.</p></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"115 ","pages":"Article 102556"},"PeriodicalIF":3.9000,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling of the COVID-19 impact on air passenger traffic in the US, European countries, and China\",\"authors\":\"Wai Ming To ,&nbsp;Peter K.C. Lee\",\"doi\":\"10.1016/j.jairtraman.2024.102556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The COVID-19 pandemic has changed many aspects of people's lives including travel since early 2020. Specifically, it has adversely affected people traveling by air and has hit the air transport industry significantly. But, how big is the COVID-19 impact? In order to answer such a question, we collected air passenger traffic data from the US, European countries, and China which accounted for over 75% of the world's total air passenger traffic. Air passenger traffic data in these three regions during the period January 2010 to December 2019 were modeled using seasonal autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models were used to predict air passenger traffic from January 2011 to December 2019 (just before the spread of COVID-19) and the accuracy of the models was evaluated. The models were then used to predict air passenger traffic from January 2020 to December 2022 for the case without COVID-19. The COVID-19 impacts on air passenger traffic were estimated by calculating the differences in predicted and actual air passenger numbers in monthly basis. Results showed that air passenger traffic was significantly recovered in the US and European countries but it encountered significant falls in 2021 and 2022 in China due to spikes in COVID-19 variant cases in many provinces and the implementation of zero-tolerance COVID-19 policy. Implications of the study are given.</p></div>\",\"PeriodicalId\":14925,\"journal\":{\"name\":\"Journal of Air Transport Management\",\"volume\":\"115 \",\"pages\":\"Article 102556\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Air Transport Management\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0969699724000218\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Air Transport Management","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0969699724000218","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0

摘要

自 2020 年初以来,COVID-19 大流行病改变了人们生活的许多方面,包括旅行。特别是,它对乘坐飞机旅行的人们造成了不利影响,并对航空运输业造成了巨大冲击。但是,COVID-19 的影响到底有多大?为了回答这个问题,我们收集了占全球航空客运总量 75% 以上的美国、欧洲国家和中国的航空客运数据。我们使用季节性自回归综合移动平均(ARIMA)模型对这三个地区 2010 年 1 月至 2019 年 12 月期间的航空客运量数据进行了建模。使用季节性 ARIMA 模型预测了 2011 年 1 月至 2019 年 12 月(COVID-19 传播前夕)的航空客运量,并对模型的准确性进行了评估。然后,在没有 COVID-19 的情况下,使用这些模型预测 2020 年 1 月至 2022 年 12 月的航空客运量。COVID-19 对航空客运量的影响是通过计算每月预测和实际航空客运量的差异来估算的。结果表明,美国和欧洲国家的航空客运量明显回升,但中国的航空客运量在 2021 年和 2022 年出现大幅下降,原因是许多省份的 COVID-19 变异病例激增,以及 COVID-19 零容忍政策的实施。本研究的意义在于
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Modeling of the COVID-19 impact on air passenger traffic in the US, European countries, and China

The COVID-19 pandemic has changed many aspects of people's lives including travel since early 2020. Specifically, it has adversely affected people traveling by air and has hit the air transport industry significantly. But, how big is the COVID-19 impact? In order to answer such a question, we collected air passenger traffic data from the US, European countries, and China which accounted for over 75% of the world's total air passenger traffic. Air passenger traffic data in these three regions during the period January 2010 to December 2019 were modeled using seasonal autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models were used to predict air passenger traffic from January 2011 to December 2019 (just before the spread of COVID-19) and the accuracy of the models was evaluated. The models were then used to predict air passenger traffic from January 2020 to December 2022 for the case without COVID-19. The COVID-19 impacts on air passenger traffic were estimated by calculating the differences in predicted and actual air passenger numbers in monthly basis. Results showed that air passenger traffic was significantly recovered in the US and European countries but it encountered significant falls in 2021 and 2022 in China due to spikes in COVID-19 variant cases in many provinces and the implementation of zero-tolerance COVID-19 policy. Implications of the study are given.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
12.40
自引率
11.70%
发文量
97
期刊介绍: The Journal of Air Transport Management (JATM) sets out to address, through high quality research articles and authoritative commentary, the major economic, management and policy issues facing the air transport industry today. It offers practitioners and academics an international and dynamic forum for analysis and discussion of these issues, linking research and practice and stimulating interaction between the two. The refereed papers in the journal cover all the major sectors of the industry (airlines, airports, air traffic management) as well as related areas such as tourism management and logistics. Papers are blind reviewed, normally by two referees, chosen for their specialist knowledge. The journal provides independent, original and rigorous analysis in the areas of: • Policy, regulation and law • Strategy • Operations • Marketing • Economics and finance • Sustainability
期刊最新文献
Stochastic infection risk models for aircraft seat assignment considering passenger vaccination status and seat location Addressing the impact of airport pricing, investment and operations on greenhouse gas emissions Editorial Board A privacy-preserving federated learning approach for airline upgrade optimization Exploring prediction accuracy for optimal taxi times in airport operations using various machine learning models
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1