Data-Driven Departure Flight Time Prediction Based on Feature Construction and Ensemble Learning

IF 1.3 4区 工程技术 Q2 ENGINEERING, AEROSPACE
Jiaxin Xu, Junfeng Zhang, Zihan Peng, J. Bao, Bin Wang
{"title":"Data-Driven Departure Flight Time Prediction Based on Feature Construction and Ensemble Learning","authors":"Jiaxin Xu, Junfeng Zhang, Zihan Peng, J. Bao, Bin Wang","doi":"10.2514/1.i011227","DOIUrl":null,"url":null,"abstract":"Temporal–spatial resource optimization within the terminal maneuvering area has become an important research direction to meet the growing demand for air traffic. Accurate departure flight time prediction from taking off to the metering fixes is critical for departure management, connecting the surface operations, and overhead stream insertion. This paper employs ensemble learning methods (including bagging, boosting, and stacking) to predict departure flight times via different metering fixes based on four feature categories: initial states, operating situation, traffic demand, and wind velocity. The stacking method employs a linear regressor, a support vector regressor, and a tree-based ensemble regressor as base learners. The Guangzhou Baiyun International Airport case study shows that the stacking method proposed in this work outperforms other methods and could achieve satisfactory performance in departure flight time prediction, with a high prediction accuracy of up to 89% within a 1 min absolute error and 98% within a 2 min absolute error. Besides, the affecting factors analysis indicates that the operation direction, flight distance, and traffic demand in different areas significantly improve prediction accuracy.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"1 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Aerospace Information Systems","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2514/1.i011227","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0

Abstract

Temporal–spatial resource optimization within the terminal maneuvering area has become an important research direction to meet the growing demand for air traffic. Accurate departure flight time prediction from taking off to the metering fixes is critical for departure management, connecting the surface operations, and overhead stream insertion. This paper employs ensemble learning methods (including bagging, boosting, and stacking) to predict departure flight times via different metering fixes based on four feature categories: initial states, operating situation, traffic demand, and wind velocity. The stacking method employs a linear regressor, a support vector regressor, and a tree-based ensemble regressor as base learners. The Guangzhou Baiyun International Airport case study shows that the stacking method proposed in this work outperforms other methods and could achieve satisfactory performance in departure flight time prediction, with a high prediction accuracy of up to 89% within a 1 min absolute error and 98% within a 2 min absolute error. Besides, the affecting factors analysis indicates that the operation direction, flight distance, and traffic demand in different areas significantly improve prediction accuracy.
基于特征构建和集成学习的数据驱动起飞时间预测
终端机动区域的时空资源优化已成为满足日益增长的空中交通需求的重要研究方向。从起飞到计量固定,准确的起飞时间预测对于起飞管理、连接地面作业和架空流插入至关重要。本文采用集成学习方法(包括bagging、boosting和stacking),基于初始状态、运行情况、交通需求和风速四个特征类别,通过不同的计量固定来预测起飞时间。叠加方法采用线性回归器、支持向量回归器和基于树的集成回归器作为基础学习器。广州白云国际机场的案例研究表明,本文提出的叠加法在离港飞行时间预测方面优于其他方法,在1 min绝对误差内预测精度可达89%,在2 min绝对误差内预测精度可达98%。此外,影响因素分析表明,不同区域的运行方向、飞行距离和交通需求显著提高了预测精度。
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
3.70
自引率
13.30%
发文量
58
审稿时长
>12 weeks
期刊介绍: This Journal is devoted to the dissemination of original archival research papers describing new theoretical developments, novel applications, and case studies regarding advances in aerospace computing, information, and networks and communication systems that address aerospace-specific issues. Issues related to signal processing, electromagnetics, antenna theory, and the basic networking hardware transmission technologies of a network are not within the scope of this journal. Topics include aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. The Journal also features Technical Notes that discuss particular technical innovations or applications in the topics described above. Papers are also sought that rigorously review the results of recent research developments. In addition to original research papers and reviews, the journal publishes articles that review books, conferences, social media, and new educational modes applicable to the scope of the Journal.
×
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