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Frontiers of Information Technology & Electronic Engineering
ISSN 2095-9184 (print), ISSN 2095-9230 (online)
2025 Vol.26 No.12 P.2421-2439
Eixão-UAM: LLM-assisted iterative design of a low-altitude urban air mobility corridor in Brasilia
Abstract: The development of urban air mobility (UAM) systems requires scalable, regulation-aware planning of low-altitude airspace and supporting infrastructure. This study proposes an end-to-end framework for the design, simulation, and iterative optimization of a structured UAM corridor over Brasilia’s central road axis (Eixão-UAM), aligned with the Brazilian unmanned aircraft traffic management (BR-UTM) ecosystem. In addition, this study proposes a multilayered aerial configuration stratified by unmanned aerial vehicle class, supported by a modular ground infrastructure composed of vertihubs, vertiports, and vertistops. A takeoff-scheduling simulator is developed to evaluate platform allocation strategies under realistic traffic and weather conditions. Initial experiments compare a round-robin (RR) baseline with a genetic algorithm (GA), and results reveal that RR outperforms GA v1 in terms of the average waiting time. To address this gap, a large language model (LLM) assisted optimization loop is implemented using GPT-4o Mini and Gemini 2.5 Pro. The LLMs act as reasoning partners, supporting the root-cause diagnoses, fitness function redesign, and rapid prototyping of five GA variants. Among these, GA v5 achieves a 59.62% reduction in maximum waiting time and an approximately 10% reduction in average waiting time over GA v1, thereby approaching the robustness of RR. In contrast, GA v2–v4 and GA v6 perform less consistently, showing an importance of fitness function design. These results underscore the role of an iterative, LLM-guided development in enhancing classical optimization, demonstrating that generative artificial intelligence (AI) can contribute to simulation acceleration and the cocreation of operational logic. The proposed method provides a replicable blueprint for integrating LLMs into early-stage UAM planning, offering both theoretical insights and architectural guidance for future low-altitude airspace systems.
Key words: Brasilia; Eixão; Genetic algorithm; Large language model (LLM); Unmanned aerial vehicle (UAV); Urban air mobility (UAM); UAM corridor; Unmanned aircraft traffic management (UTM)
1巴西利亚大学计算机系TransLab,巴西巴西利亚,70910-900
2巴西利亚大学信息科学学院,巴西巴西利亚,70910-900
摘要:城市空中交通(UAM)系统的发展需要对低空空域及配套基础设施进行可扩展且符合法规的规划。本研究提出了一种端到端框架,用于设计、仿真和迭代优化巴西利亚中心道路轴线(Eix?o)的低空UAM走廊(Eix?o-UAM)上的结构化UAM航道,该框架与巴西无人机交通管理(BR-UTM)生态体系相兼容。此外,本研究提出了一种基于无人机等级分层的多层空中配置方案,该方案由垂直起降枢纽、垂直起降港和垂直起降站组成的模块化地面基础设施提供支持。开发了起飞调度仿真模型,用于在真实交通和气象条件下评估平台分配策略。初步实验将循环调度(RR)基准方案与遗传算法(GA)进行对比,结果表明RR在平均等待时间方面优于GA v1版本。为弥补这一差距,采用GPT-4o Mini和Gemini 2.5 Pro实施了大语言模型(LLM)辅助优化循环。这些语言模型作为推理伙伴,支持对5个遗传算法变体的根本原因诊断、适应度函数重构及快速原型设计。其中,相较于GA v1,GA v5的最大等待时间降低了59.62%,平均等待时间降低了约10%,其鲁棒性已接近RR算法。相比之下,GA v2‐v4及GA v6表现出较低的一致性,凸显了适应度函数设计的重要性。这些结果凸显了LLM协助的迭代开发在增强经典优化中的作用,证明生成式人工智能(AI)可助力仿真加速与运行逻辑协同创建。该方法为将LLM融入UAM早期规划提供了可复制的蓝图,为未来低空空域系统提供了理论洞见和架构指导。
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DOI:
10.1631/FITEE.2500541
CLC number:
TP31
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On-line Access:
2026-01-09
Received:
2025-07-30
Revision Accepted:
2025-10-20
Crosschecked:
2026-01-12