通过灰狼优化的轨迹调整来减轻移动部门的空中交通复杂性

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Simon Bruno Göppel, Ehsan Asadi, Michael Schultz
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引用次数: 0

摘要

本研究引入了一种新的方法来优化移动部门内的空中交通复杂性,这是一种为以流为中心的空中交通管理而设计的概念。移动扇区通过将具有相似轨迹和相互作用的飞机分组来动态分配控制器工作量。提出了一种结合灰狼优化的轨迹调整方法,通过少量的横向路径修改来降低交通复杂性。这种方法保持了操作限制,如切换时间,同时确保对飞机轨迹的干扰最小。两个案例研究显示了显著的改进,平均流量复杂性降低了27%,峰值复杂性负载降低了30%。这些发现突出了以流量为中心的程序在提高空域容量、确保未来空中交通管理系统的安全和效率方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trajectory adjustments via grey wolf optimization to mitigate air traffic complexity in moving sectors
This study introduces a novel approach to optimizing air traffic complexity within moving sectors, a concept designed for flow-centric air traffic management. Moving sectors dynamically allocate controller workload by grouping aircraft with similar trajectories and interactions. A trajectory adjustment method is proposed, incorporating Grey Wolf Optimization, to reduce traffic complexity through minor lateral path modifications. This approach maintains operational constraints, such as handover times, while ensuring minimal disruption to aircraft trajectories. Two case studies demonstrate significant improvements, with reductions in average traffic complexity of up to 27% and peak complexity loads of up to 30%. These findings highlight the potential of flow-centric procedures to enhance airspace capacity, ensuring safety and efficiency in future air traffic management systems.
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来源期刊
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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