预测非阻塞延误的机器学习：巴黎戴高乐国际机场案例研究

IF 2.7 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Data & Knowledge Engineering Pub Date : 2024-05-08 DOI:10.1016/j.datak.2024.102303

Thibault Falque , Bertrand Mazure , Karim Tabia

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引用次数: 0

摘要

准点率是大型机场和枢纽的一个敏感问题，关系到乘客体验和运营成本控制。本文提出了一个具有挑战性的实际问题，即如何预测和解释航班延误。我们以巴黎戴高乐国际机场（Paris Charles de Gaulle，简称 "Paris-CDG"）为例进行研究，从巴黎戴高乐机场这一问题的特殊性入手，到提出模型和解决方案，再到对全年活动的真实数据进行结果分析。本文所提供的概念证明让我们相信，所提出的方法有助于改善延误管理并减少由此造成的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Machine learning for predicting off-block delays: A case study at Paris — Charles de Gaulle International Airport

Punctuality is a sensitive issue in large airports and hubs for passenger experience and for controlling operational costs. This paper presents a real and challenging problem of predicting and explaining flight off-block delays. We study the case of the international airport Paris Charles de Gaulle (Paris-CDG) starting from the specificities of this problem at Paris-CDG until the proposal of modelings then solutions and the analysis of the results on real data covering an entire year of activity. The proof of concept provided in this paper allows us to believe that the proposed approach could help improve the management of delays and reduce the impact of the resulting consequences.

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来源期刊

Data & Knowledge Engineering 工程技术-计算机：人工智能

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

6 months

期刊介绍： Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.