On-demand assessment of air traffic impact of blocking airspace

作     者：Srivastava, A. St Clair, T. Pan, G. 

作者机构：MITRE Corp Mclean VA 22102 USA 

出 版 物：《AERONAUTICAL JOURNAL》 (Aeronaut J)

年 卷 期：2018年第122卷第1258期

页      面：1985-2009页

核心收录：

学科分类：08[工学] 0825[工学-航空宇航科学与技术] 

主　　题：NAS impact What-if analysis air traffic projection data reduction data encoding traffic pattern blocked airspaces CDM commercial Space 

摘      要：The Federal Aviation Administration often blocks strategically located airspace volumes to ensure safety during a variety of operations that are potentially hazardous to aircraft, such as space launches. As the frequency of these operations increases, there is a growing need to deepen collaboration and transparency between stakeholders regarding the use of airspace. This collaboration can be supported by models and capabilities to quickly assess the impact of airspace closures, up to 12 months into the future. This paper presents a technique to enable a what-if analysis capability coupled with a prediction model, whereby changes in airspace dimension, location, and activation time are reflected instantaneously as measures of projected impact. The technique can also be used for quick post-operations analysis using historical traffic data and to develop air traffic impact assessment capabilities accessible to a broad range of users outside of the air traffic domain. This research has three key components: developing a model to predict air traffic demand up to 12 months into the future, modelling air traffic impact to the affected traffic, and reducing this information into a data structure that can support on-demand analysis. The focus of this paper is on new techniques to predict demand using a large set of historical track data and further encode these projections to support the quick assessment of the impact of blocking various airspace volumes. Initial results show that the proposed data reduction scheme accurately represented the traffic crossing an airspace and resulted in data size reduction by over 50%. The projection model performed well, the actual number of impacted flights were within the estimated range of approximately 80% of the time. Finally, the responsiveness of the web-based prototype developed to illustrate the concept demonstrated the model s ability to support an on-demand assessment of the air traffic impact of blocking airspace. A significant