Diagnostic throughput factor analysis for en-route airspace and optimal aircraft trajectory generation based on capacity prediction and controller workload

Sanghyun Shin, Purdue University


Today's National Airspace System (NAS) is approaching its limit to efficiently cope with the increasing air traffic demand. Next Generation Air Transportation System (NextGen) with its ambitious goals aims to make the air travel more predictable with fewer delays, less time sitting on the ground and holding in the air to improve the performance of the NAS. However, currently the performance of the NAS is mostly measured using delay-based metrics which do not capture a whole range of important factors that determine the quality and level of utilization of the NAS. The factors affecting the performance of the NAS are themselves not well defined to begin with. To address these issues, motivated by the use of throughput-based metrics in many areas such as ground transportation, wireless communication and manufacturing, this thesis identifies the different factors which majorly affect the performance of the NAS as demand (split into flight cancellation and flight rerouting), safe separation (split into conflict and metering) and weather (studied as convective weather) through careful comparison with other applications and performing empirical sensitivity analysis. Additionally, the effects of different factors on the NAS's performance are quantitatively studied using real traffic data with the Future ATM Concepts Evaluation Tool (FACET) for various sectors and centers of the NAS on different days. In this thesis we propose a diagnostic tool which can analyze the factors that have greater responsibility for regions of poor and better performances of the NAS. Based on the throughput factor analysis for en-route airspace, it was found that weather and controller workload are the major factors that decrease the efficiency of the airspace. Also, since resources such as air traffic controllers, infrastructure and airspace are limited, it is becoming increasingly important to use the available resources efficiently. To alleviate the impact of the weather and controller workload while optimally utilizing limited resources, various aircraft rerouting strategies for Air Traffic Management (ATM) have been proposed. However, the number of rerouting tools available to address these issues for the center-level and the National Airspace System (NAS) are relatively less compared with the tools for the sector-level and terminal airspace. Additionally, previous works consider the airspace containing the weather as no-fly zones instead of reduced-traffic zones and do not explicitly consider controller workload when generating aircraft trajectories to avoid the weather-affected airspace, thereby reducing the overall performance of the airspace. In this thesis, a new rerouting algorithm for the center-level airspace is proposed to address these problems by introducing a feedback loop connecting a tactical rerouting algorithm with a strategic rerouting algorithm using dynamic programming and a modified A* algorithm respectively. This helps reduce the computational cost significantly while safely handling a large number of aircraft. In summary, this thesis suggests the ways in which the NAS's performance can be further improved, thereby supporting various concepts envisioned by the Next Generation Air Transportation System (NextGen) and providing vital information which can be used for suitable economic and environmental advantages.




Hwang, Purdue University.

Subject Area

Aerospace engineering

Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server