Air traffic controller supervisors configure available sector, operating position, and workstation resources to safely and efficiently control air traffic in a region of airspace. In this paper, an algorithm for assisting supervisors with this task is described and demonstrated on two sample problem instances. The algorithm produces configuration schedule advisories that minimize a cost. The cost is a weighted sum of two competing costs: one penalizing mismatches between configurations and predicted air traffic demand and another penalizing the effort associated with changing configurations. The problem considered by the algorithm is a shortest path problem that is solved with a dynamic programming value iteration algorithm. The cost function contains numerous parameters. Default values for most of these are suggested based on descriptions of air traffic control procedures and subject-matter expert feedback. The parameter determining the relative importance of the two competing costs is tuned by comparing historical configurations with corresponding algorithm advisories. Two sample problem instances for which appropriate configuration advisories are obvious were designed to illustrate characteristics of the algorithm. Results demonstrate how the algorithm suggests advisories that appropriately utilize changes in airspace configurations and changes in the number of operating positions allocated to each open sector. The results also demonstrate how the advisories suggest appropriate times for configuration changes.
This paper compares airspace design solutions for dynamically reconfiguring airspace in response to nominal daily traffic volume fluctuation. Airspace designs from seven algorithmic methods and a representation of current day operations in Kansas City Center were simulated with two times today's demand traffic. A three-configuration scenario was used to represent current day operations. Algorithms used projected unimpeded flight tracks to design initial 24-hour plans to switch between three configurations at predetermined reconfiguration times. At each reconfiguration time, algorithms used updated projected flight tracks to update the subsequent planned configurations. Compared to the baseline, most airspace design methods reduced delay and increased reconfiguration complexity, with similar traffic pattern complexity results. Design updates enabled several methods to as much as half the delay from their original designs. Freeform design methods reduced delay and increased reconfiguration complexity the most.
By examining the relative benefit of reconfigured airspace to the original airspace under the same traffic conditions, this paper assessed Flexible Airspace Management that reconfigures airspace boundaries. Using weather rerouted flight plans, four airspace design methods reconfigured the original airspace design in Kansas City Center. Air traffic simulations with estimated NextGen midterm (2018) airport capacities and traffic demand were performed for the original and each reconfigured airspace design. Analysis showed that within the simulated scenarios, reconfigured airspace demonstrated user benefit by decreasing 68% of the number of flights needing to be delayed or turned away from entering the airspace to maintain balance between traffic demand and capacity. Utilization of available air traffic control resources increased by 8%, demonstrating service provider benefit. Airspace design methods that applied more changes to the original airspace achieved more benefit. However, increased change from the original airspace configuration implied a possible increase in air traffic controller workload during the transition from the original to the reconfigured airspace.
Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1][2][3][4][5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6][7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts.Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback.The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity.https://ntrs.nasa.gov/search.jsp?R=20110003568 2018-05-11T10:45:28+00:00ZThis method was used to select a dynamic configuration baseline for Kansas City Center (ZKC) for a good weather, high volume day. A total of 78 configurations were used at some time in Kansas City Center on February 8, 2007. The clustering algorithm was applied to the 78 configuration schedule with k ranging from one to six. Preliminary results show that the overall stability score improves rapidly until the three-configuration schedule. For this day, the three-configuration schedule yields the best accuracy for the increased scenario complexity, and the two configuration triggering times are 2007/02/08 12:19:21 UTC and 2007/02/09 00:58:07 UTC.The final version of this paper will include an analysis of reconf...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.