Abstract:In Next Generation Air Transportation System (NextGen) operations, we expect that the demand-capacity balance can be achieved by selectively managing the airspace capacity in conjunction with managing the traffic demand. In Flexible Airspace Management (FAM), the airspace complexity can be assessed a few hours ahead in order to identify sectors that could exceed their defined traffic threshold as well as sectors that are under-utilized. Using various airspace optimization algorithms, airspace can be reconfigur… Show more
“…A previous study of airspace reconfiguration impact on air traffic controller workload indicated that a decrease in similarity between the original and reconfigured airspace was related to an increase in controller workload during the airspace reconfiguration [15,16]. The similarity between airspace configuration i and the original was calculated as a similarity distance, D i , where larger distance indicates less similarity.…”
Section: Airspace Similaritymentioning
confidence: 99%
“…Previous studies on airspace design indicated that increased number of flights with short dwell time in a sector was related to increased controller workload [15,16]. Let T i be the average number of flights with short dwell time in airspace configuration i, given by…”
Section: Number Of Flights With Short Dwell Timementioning
confidence: 99%
“…Studies also indicated that a decrease in the average distance between traffic crossing points and airspace boundary was related to an increase in air traffic controller workload [15,16]. Let X i be the average traffic crossing point's distance to airspace boundary in airspace configuration i, given by …”
Section: Distance Between Traffic Crossing Points and Airspace Boundarymentioning
Flexible Airspace Management (FAM) concept offers to dynamically modify the center/sector boundaries in such way that the airspace structure is reconfigured to better distribute unbalanced traffic demands across sectors. A set of airspace design algorithms were used in the human-in-the-loop simulation to assess possible benefits of the FAM concept. In the simulation, participants were instructed to pick an algorithm-generated airspace configuration from a set of configuration options that best solved the weather-induced traffic imbalance problems in the test airspace. Participants also rated the acceptability of the airspace designs that were generated by different algorithms. This paper explores ways to objectively quantify airspace characteristics of these algorithm-generated configurations using a set of benefits and airspace quality metrics and to compare them to the participants' acceptability ratings obtained from the simulation. Both benefits and airspace quality metrics were hypothesized to correlate with the participants' ratings. The results showed that participants' selection correlated mainly with the benefits metrics, while airspace quality metrics did not play a big role.
“…A previous study of airspace reconfiguration impact on air traffic controller workload indicated that a decrease in similarity between the original and reconfigured airspace was related to an increase in controller workload during the airspace reconfiguration [15,16]. The similarity between airspace configuration i and the original was calculated as a similarity distance, D i , where larger distance indicates less similarity.…”
Section: Airspace Similaritymentioning
confidence: 99%
“…Previous studies on airspace design indicated that increased number of flights with short dwell time in a sector was related to increased controller workload [15,16]. Let T i be the average number of flights with short dwell time in airspace configuration i, given by…”
Section: Number Of Flights With Short Dwell Timementioning
confidence: 99%
“…Studies also indicated that a decrease in the average distance between traffic crossing points and airspace boundary was related to an increase in air traffic controller workload [15,16]. Let X i be the average traffic crossing point's distance to airspace boundary in airspace configuration i, given by …”
Section: Distance Between Traffic Crossing Points and Airspace Boundarymentioning
Flexible Airspace Management (FAM) concept offers to dynamically modify the center/sector boundaries in such way that the airspace structure is reconfigured to better distribute unbalanced traffic demands across sectors. A set of airspace design algorithms were used in the human-in-the-loop simulation to assess possible benefits of the FAM concept. In the simulation, participants were instructed to pick an algorithm-generated airspace configuration from a set of configuration options that best solved the weather-induced traffic imbalance problems in the test airspace. Participants also rated the acceptability of the airspace designs that were generated by different algorithms. This paper explores ways to objectively quantify airspace characteristics of these algorithm-generated configurations using a set of benefits and airspace quality metrics and to compare them to the participants' acceptability ratings obtained from the simulation. Both benefits and airspace quality metrics were hypothesized to correlate with the participants' ratings. The results showed that participants' selection correlated mainly with the benefits metrics, while airspace quality metrics did not play a big role.
“…Main contributors to controller workload during the boundary change were initially identified. 32 Both areas, mixed operations in the same airspace and flexible airspace management are described below.…”
Section: Dynamic Airspace Configuration: Mixed Operations and Flexmentioning
confidence: 99%
“…Overall, the results and feedback from the study showed that Flexible Airspace is a promising concept worth further development and refinement. 32,43 A number of tradeoffs may be required in finding the most effective way to address the demand-capacity imbalance while keeping the human controller integrated and functioning meaningfully within the system. Based on the results from this study, further research can begin in addressing these issues.…”
Air traffic management and airspace management reduce air traffic congestion to maintain safety. Managing traffic induces costs on airspace users and managing airspace causes additional work for air traffic controllers. This paper proposes and simulates algorithms for tactically reducing airspace congestion with coordinated air traffic and airspace management. A modified version of the Projective Cone Scheduling algorithm performs tactical air traffic management. An algorithm based on approximate dynamic programming accomplishes tactical airspace management. Three types of coordination between these air traffic and airspace management algorithms are investigated. Monte Carlo simulations of a simple problem instance involving severe congestion indicate that increased coordination between air traffic and airspace management can lead to lower costs with no increase in algorithm computation time.
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