Analysis and Modeling of Miles-in-Trail Restrictions in the National Airspace System

Abstract

This paper presents an analysis of values and locations of Miles-in-Trail restrictions used within the National Airspace System over the last three years. Using specific severe weather avoidance routes, various locations are selected to implement the Miles-in-Trail restrictions to study their individual impact on the delay of flights and sector congestion in the airspace. The current traffic management operational infrastructure lacks the modeling of multiple restrictions with passback Miles-in-Trail values to upstream facilities. The model developed here allows implementation of multiple restriction locations for multiple merging streams of traffic. The model also permits speed control, vectoring or airborne holding, and passback of restrictions to upstream facilities. The simulation environment allows implementation of these restrictions, enabling a what-if capability in a rapid evaluation mode for Miles-in-Trail impact. Preliminary results are presented for delay of impacted flights due to implementation of three different playbook routes and Miles-in-Trail values at various locations with passbacks to upstream facilities. Results of sector congestion in the airspace for those cases are discussed as well. It was observed that for a particular playbook route implementation with Miles-in-Trail between 25 and 30 nmi applied at a reference fix resulted in low total delay and sector congestion. Overall, the model appears to be a good starting point for evaluation of passback restriction impact and, with operational feedback, could be used for advising passback values to upstream facilities.

Development of miles-in-trail passback restrictions for air traffic management

“…The model presented in this paper extends the research presented in [8]. The new model can handle multiple merging streams of traffic simultaneously, MIT values at multiple locations across those traffic streams, and optional passback to an upstream Center, along with imposition of maximum ground delay and absorbable airborne delay for flights.…”

“…Figure 1 shows a snapshot of FACET graphical user interface. Three FAA published and most used [8] Playbook routes during 2010-2012, CAN_1_East, VUZ, and FL2NE1, are shown. They are shown in green, along with some of the fixes associated with the CAN_1_East route, e.g.…”

“…In the earlier paper, Sheth et al [8] presented a model for computing MIT and passback restrictions in the NAS for current traffic conditions. The focus of this paper is in extending the model presented there with additional capabilities and traffic manager feedback.…”

Research on Performance Analysis of Air Traffic Flow Management Combined Strategy Impacted by uncertainty

“…随着亚太地区经济的快速增长， 中国南海地区的飞行量与日俱增， 加之洋区的恶劣天气， 导致空中交通拥堵频发。当三亚管制区下游出区域点受到外界流控时，为解决下游尾随间隔 (Minute-in-Trail，MIT)向上游放大传播，导致泰国等机场起飞航班受到过大的流控限制的 问题，三亚管制区将建立尾随间隔限制与起飞时隙协同管理程序，对相邻国家经过三亚管制 区域受流控影响的航班采用不同的流量管理措施分别实施管理，对越南等国家在进三亚管制 区的移交点发布尾随间隔限制，对泰国等机场起飞的航班直接发布计算起飞时间(Calculated Takeoff Time，CTOT)，以满足出三亚管制区的下游流控限制。针对上述问题，需要建立多 策略综合决策模型，对尾随间隔以及起飞时间进行综合决策管理。同时，由于航班在策略执 行过程中存在不确定性，这些不确定性对策略的实施效能会产生影响，需要针对不确定性对 组合策略效能的影响进行分析。 国外对起飞时隙分配的研究起源于20世纪90年代，美国的地面等待程序中采用RBS (Ration by schedule)算法进行起飞时隙分配，RBS算法是按照先到先服务顺序来进行时隙分 配，并没有考虑不同航班的延误时间和延误类型与延误成本之间的关系。Vossen和Ball通过设 置成本系数建立了Optiflow的时隙分配模型和时隙交换模型 [1] 。 欧洲ETFMS流量管理系统采用 CASA算法考虑多元受限下的起飞时隙分配，航班根据所受到的最严格的约束分配起飞时隙。 在尾随间隔限制策略研究方面，Sheth收集分析美国繁忙空域交通流数据，统筹考虑了尾 随间隔对上游交通流的影响 [2][3] 。Mukherjee重点研究延误的分配问题，以降低延误为目标建 立了一种新的间隔优化模型 [4] 。孙樊荣等将交通复杂性转化为管制工作负荷，建立了管制移 交策略优化模型 [5] 。 在多策略综合优化研究方面，Wank通过排队模型的仿真来研究多策略交互的影响 [6] 。 Rebollo采用分析法研究了多策略交互影响，并在布朗运动公式的基础上量化了流量管理策略 之间的相互作用 [7] 。 Meyn提出一种封闭式算法， 解决了航班受多重限制时的时隙分配问题 [8] 。 Cynthia提出了在机场容量受限的情况下，控制进场航班顺序变动次数优化航班排序，并利用 线性规划方法将该策略运用到多机场时隙分配中进行了仿真试验 [9] 。 Churchill研究了将单元受 限下的RBS分配扩展用于多个受限单元时隙资源分配的方法 [10] 。但是当前尚没有尾随间隔策 略与起飞时隙分配策略综合决策优化的研究。徐冬慧研究了地面等待和尾随间隔限制组合策 略的确定性模型建模及求解 [11] 。 在流量管理策略效能分析研究方面，Adrian Agogino分析了当存在离场不确定性时0/1规 划方法以及进化算法两种交通管理方法的鲁棒性 [12] ， Lulli和Odoni通过简单的例子分析了多个 管理程序所产生的最小化总延误和保持先计划先服务公平性目标之间的冲突 [13] 。 本文针对南中国海地区跨国界流量管理应用场景，在基于地面等待与尾随间隔综合策略 优化模型生成的管理策略基础上 [11]…”

Scheduling and Delivering Aircraft to Departure Fixes in the NY Metroplex with Controller-Managed Spacing Tools

“…Commonly, en route TMCs manage excess demand by placing Miles-In-Trail (MIT) flow restrictions to departure fixes. [7][8][9] These restrictions are one of the TMIs available for traffic management. MIT are often imposed by downstream en route centers which apply restrictions at their boundary to upstream centers to manage the demand in their own sectors.…”