Climate-aware air traffic flow management optimization via column generation

IF 7.6 1区工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY

Transportation Research Part C-Emerging Technologies Pub Date : 2024-08-06 DOI:10.1016/j.trc.2024.104792

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引用次数: 0

Abstract

Aviation is one of the global warming contributors. Its impact is due to CO $_{2}$ and non-CO $_{2}$ effects. Trajectory design is one of action levers for minimizing the environmental impact of air transportation. However, it affects the Air Traffic Management and should satisfy airspace constraints, especially airspace capacities. This paper proposes a climate-aware version of the Air Traffic Flow Management (ATFM), focusing on CO $_{2}$ and one particular non-CO $_{2}$ effect: condensation trails (contrails), although other non-CO $_{2}$ effects can be integrated. A deterministic ATFM optimization model is proposed, solved by a column generation approach. This problem is solved using different metrics, from simple to more complex and realistic ones. Numerical experiments are conducted both in the lateral case and when the cruise altitude becomes a decision variable. The impact of airspace capacities is also evaluated. The problem instances that are studied are built from realistic open-access data and made publicly available.

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通过列生成优化气候感知型空中交通流量管理

航空是导致全球变暖的因素之一。其影响来自二氧化碳和非二氧化碳效应。轨迹设计是将航空运输对环境的影响降至最低的行动杠杆之一。然而，它影响空中交通管理，应满足空域限制，特别是空域容量。本文提出了气候感知版本的空中交通流量管理（ATFM），重点关注二氧化碳和一种特殊的非二氧化碳效应：凝结尾迹（倒飞），当然也可以整合其他非二氧化碳效应。我们提出了一个确定性 ATFM 优化模型，并通过列生成方法加以解决。这个问题的解决采用了不同的指标，从简单到更复杂、更现实的指标。在横向情况下和巡航高度成为决策变量时都进行了数值实验。此外，还评估了空域容量的影响。所研究的问题实例是根据现实的开放数据建立的，并已公开发布。

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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来源期刊

Transportation Research Part C-Emerging Technologies 工程技术-运输科技

CiteScore

15.80

自引率

12.00%

发文量

332

审稿时长

64 days

期刊介绍： Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.

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