基于地面保障流程的过站航班延误预测方法

doi:10.3969/j.issn.1674-0696.2023.09.17

重庆交通大学学报（自然科学版） ›› 2023, Vol. 42 ›› Issue (9): 122-129.DOI: 10.3969/j.issn.1674-0696.2023.09.17

• 交通基础设施工程 • 上一篇

基于地面保障流程的过站航班延误预测方法

羊钊1，陈怡欣2，张智杰2

(1. 南京航空航天大学通用航空与飞行学院，江苏溧阳 213300； 2. 南京航空航天大学民航学院，江苏南京 211116)

收稿日期:2022-06-15 修回日期:2023-01-17 发布日期:2023-10-16
作者简介:羊钊（1988—），女，江苏靖江人，副教授，博士，主要从事大数据与深度学习方面的研究。E-mail:yangzhao@nuaa.edu.cn 通信作者：陈怡欣(1998—)，女，陕西咸阳人，硕士，主要从事航班延误与机器学习方面的研究。E-mail:18191213058@163.com
基金资助:
国家自然科学基金项目(52172328)

Delay Prediction Method of Transit Flight Based on Ground Guarantee Process

YANG Zhao1, CHEN Yixin2, ZHANG Zhijie2

(1.College of General Aviation and Flight, Nanjing University of Aeronautics & Astronautics, Liyang 213300, Jiangsu, China; 2. College of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, Jiangsu, China)

Received:2022-06-15 Revised:2023-01-17 Published:2023-10-16

摘要/Abstract

摘要： 目前民航航班持续高位运行，航班过站时机场、空管、航司多方同时交织参与，高峰时段机场地面保障系统满负荷运转，航班正点率难以提升。为了提前感知过站航班在地面保障时多流程进行协作时产生的延误，针对航班作业流程节点的相关特征，提出基于地面保障流程的过站航班延误预测方法。将航班保障流程构建为图网络结构，采用各流程节点上处理后的时间特征作为图卷积神经网络的各节点特征，针对节点特征采用多种聚合传递方式并进行集成，实现航班延误预测精度的提升。结果表明，提出的航班延误预测方法的平均预测误差降低至7.11 min，具有更好的泛化能力。

关键词: 交通运输工程；航班保障流程；时间特征处理；图神经网络集成；过站航班；延误预测

Abstract: At present, civil aviation flights continue to operate at a high level, and the airport, air traffic control and airline companies participate in the flight transit at the same time. The airport ground support system operates at full load during peak hours, and the flight punctuality rate is difficult to improve. In order to perceive the delays caused by multi-process cooperation during ground support for transit flights in advance, aiming at the relevant characteristics of flight operation process nodes, the flight delay prediction method based on the ground guarantee process for transit flights was proposed. The flight guarantee process was constructed as a graph convolutional neural network structure, the processed time features on each process node were used as the node features of the graph neural network. For the node features on the graph, a variety of aggregation delivery methods were used and integrated to improve the accuracy of flight delay prediction. The results show that, compared with the comparison methods, the average prediction error of the proposed flight delay prediction method is reduced to 7.11 minutes, which has better generalization ability.

Key words: traffic and transportation engineering; flight guarantee process; time feature processing; graph neural network integration; transit flights; delay prediction

中图分类号:

V351

羊钊1，陈怡欣2，张智杰2. 基于地面保障流程的过站航班延误预测方法[J]. 重庆交通大学学报（自然科学版）, 2023, 42(9): 122-129.

YANG Zhao1, CHEN Yixin2, ZHANG Zhijie2. Delay Prediction Method of Transit Flight Based on Ground Guarantee Process[J]. Journal of Chongqing Jiaotong University(Natural Science), 2023, 42(9): 122-129.

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基于地面保障流程的过站航班延误预测方法

Delay Prediction Method of Transit Flight Based on Ground Guarantee Process

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