发表机构
University of California at Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于随机排队模型的垂直起降场设计与容量分析方法，通过两节点网络模型解析吞吐量受起降与周转交互及参数方差制约，并确定最优停车坪数量以支持高效城市空中交通部署。
AI 中文摘要
本文提出了一种使用随机排队模型进行垂直起降场设计与容量分析的综合方法。垂直起降场容量定义为在保持指定最大排队延迟高概率的前提下，每小时飞机的最大稳态吞吐量。我们引入了一个具有有限缓冲区的两节点排队网络模型，以解析推导单TLOF垂直起降场的平均队列长度和延迟，并辅以仿真模型，在各种运行条件下验证了这些发现。我们的分析表明，垂直起降场容量受到起降操作与周转活动之间相互作用的显著制约。容量还因这些运行参数的方差而进一步降低，而确定性时间则大幅提升容量。研究表明，在马尔可夫模型中，垂直起降场吞吐量通常约为理论最大离场容量的50%，并随着对最大排队延迟置信度的增加而急剧下降。具有足够停车坪的非马尔可夫模型可实现完整的TLOF容量。此外，我们的方法确定了最大化单位面积吞吐量所需的最优停车坪数量。这些发现为设计支持可扩展且高效的城市空中交通部署的垂直起降场基础设施提供了见解。
英文摘要
This paper presents a comprehensive methodology for vertiport design and capacity analysis using stochastic queueing models. Vertiport capacity is defined as the maximum steady-state throughput of aircraft per hour while maintaining a specified maximum queuing delay with high probability. We introduce a two-node queueing network model with a finite buffer to analytically derive mean queue lengths and delays at singleTLOF vertiports, supported by a simulation model that validates these findings under various operational conditions. Our analyses reveal that vertiport capacity is significantly constrained by the interactions between takeoff/landing operations and turnaround activities. Capacity is further reduced by the variance in these operational parameters, with deterministic times substantially enhancing capacity. The study shows that vertiport throughput is typically around 50% of the theoretical maximum departure capacity in a Markovian model, decreasing sharply as confidence in maximum queuing delay increases. Non-Markovian models with adequate parking pads achieve full TLOF capacity. Additionally, our methodology identifies the optimal number of parking pads required to maximize throughput per unit area. The findings provide insights for designing vertiport infrastructure to support scalable and efficient UAM deployment.
