Flight delays occur when demand for capacity-constrained airspace or airports exceeds predicted capacity. Demand for capacity-constrained airspace or airports can be controlled by a series of Traffic Management Initiatives (TMIs), which use departure and airborne delays, as well as pre-departure and airborne reroutes, to manage access to the constrained resources. Two systems exist in current and planned future operations to address imbalances between demand and capacity. The Collaborative Trajectory Options Program (CTOP) reduces demand to constrained resources by assigning strategic departure delay and predeparture reroutes. Reroutes are selected from Trajectory Options Sets (TOSs) submitted by airlines. As flights approach the constrained resource, the Time-Based Flow Management System (TBFM) is used to assign tactical delay to satisfy constraints. This paper describes experiments performed to study the impact of varying levels of airline participation in CTOP via submission of TOSs on ground delay and flight time, and the impact of departure uncertainty on TBFM delays. Results suggest that as CTOP participation increases, average ground delays decrease for all airlines, but to the greatest extent for airlines participating in CTOP. A threshold in CTOP participation, which varies with the constraint capacity, is identified beyond which there is relatively little further reduction in average ground delays. Similarly, given the likely level of CTOP participation, the capacity reduction for which CTOP would be an appropriate TMI is also identified. Results also suggest that high average departure errors and high variability in departure error can make the prioritization of TBFM internal departures in TBFM metering and scheduling infeasible. Departure errors at current levels are, however, acceptable.
[1]
Min Xue,et al.
Optimized Route Capability (ORC) Intelligent Offloading of Congested Arrival Routes
,
2016
.
[2]
Richard DeLaura,et al.
Trajectory Clustering and Classification for Characterization of Air Traffic Flows
,
2016
.
[3]
Kenneth D. Mease,et al.
Automated Route Clustering for Air Traffic Modeling
,
2017
.
[4]
Paul U. Lee,et al.
Required time of arrival as a control mechanism to mitigate uncertainty in arrival traffic demand management
,
2016,
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC).
[5]
Thomas Prevot.
Exploring the Many Perspectives of Distributed Air Traffic Management: The Multi Aircraft Control System MACS
,
2002
.