Parametric Analysis of Landing Capacity for UAV Fleet Operations with Specific Airspace Structures and Rule-Based Constraints

As Urban Air Mobility (UAM) moves toward implementation, managing high-density, high-volume flights in urban airspaces becomes increasingly critical. In such environments, the design of vertiport airspace structures plays a key role in determining how many UAVs can operate safely and efficiently within a specific airspace. Existing studies have not fully explored the complex interdependencies between airspace structure parameters and fleet operation capacity, particularly regarding how various structural components and their configurations affect UAV fleet performance. This paper addresses these gaps by proposing a multi-layered funnel-shaped airspace structure for vertiports, along with an adjustable parameter model to assess factors affecting landing capacity. The proposed design includes the assembly layer, upper layer, lower layer, and approach point, forming the basis for fleet operations, divided into three phases: arrival, approach, and landing. By modeling fleet operations with various constraints and time-based algorithms, simulations have been conducted to analyze the impact of changing airspace structure parametric dimensions on UAV fleet operation capacity. The results reveal that fleet capacity is closely influenced by two limitations: the distance traveled in each phase and the availability of holding points at each layer. These findings provide valuable insights and contribute to future airspace design efforts for UAM vertiports.

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Publication Details

Journal
Drones
Published
2024-12-19
DOI
https://doi.org/10.3390/drones8120770
Primary Topic
Air Traffic Management and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Parametric Analysis of Landing Capacity for UAV Fleet Operations with Specific Airspace Structures and Rule-Based Constraints

Peng Han, Kin Huat Low, Yifei Zhao, Xinyue Yang
Drones
Air Traffic Management and Optimization
article

Parametric Analysis of Landing Capacity for UAV Fleet Operations with Specific Airspace Structures and Rule-Based Constraints

Peng Han, Kin Huat Low, Yifei Zhao, Xinyue Yang
article en

Abstract

As Urban Air Mobility (UAM) moves toward implementation, managing high-density, high-volume flights in urban airspaces becomes increasingly critical. In such environments, the design of vertiport airspace structures plays a key role in determining how many UAVs can operate safely and efficiently within a specific airspace. Existing studies have not fully explored the complex interdependencies between airspace structure parameters and fleet operation capacity, particularly regarding how various structural components and their configurations affect UAV fleet performance. This paper addresses these gaps by proposing a multi-layered funnel-shaped airspace structure for vertiports, along with an adjustable parameter model to assess factors affecting landing capacity. The proposed design includes the assembly layer, upper layer, lower layer, and approach point, forming the basis for fleet operations, divided into three phases: arrival, approach, and landing. By modeling fleet operations with various constraints and time-based algorithms, simulations have been conducted to analyze the impact of changing airspace structure parametric dimensions on UAV fleet operation capacity. The results reveal that fleet capacity is closely influenced by two limitations: the distance traveled in each phase and the availability of holding points at each layer. These findings provide valuable insights and contribute to future airspace design efforts for UAM vertiports.

DronesVol. 8(12)
Civil Aviation University of China (CN)
National Natural Science Foundation of China, Civil Aviation Administration of China
Sustainable cities and communities
Openalex Percentile: Top 61%
Air Traffic Management and Optimization
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Parametric Analysis of Landing Capacity for UAV Fleet Operations with Specific Airspace Structures and Rule-Based Constraints — Peng Han, Kin Huat Low, et al. · Drones (2024) | TGRS Research Map | TGRS