Free Flight En Route Metrics Mike Bennett The CNA Corporation
The Free Flight Metrics Team FAA Dave Knorr, Ed Meyer, Antoine Charles, Esther Hernandez, Ed Jennings CNA Corporation Joe Post, Mike Bennett, James Bonn, Dan Howell, Ashish Khatta, Dan Murphy, Tony Rubiera JTA Dale Peterman, Dave Bartlett DI Ed Freeman Other Support NEXTOR, Northrup/Grumman, MITRE/CAASD, RPI, Aerospace
What we do Free Flight Tools URET TMA CPDLC CDM Estimate potential benefits pool Future benefits projection Investment Analyses OMB Exhibit 3 Post-implementation measurement of impact
En Route Metrics Tie projected benefits to observable metrics Excess distance (compared to great circle) Primary metric for en route Flight times Wind-adjusted Excess distance and flight time by phase of flight Lines data Flight Plan Amendments En Route Throughput Delay Distance savings from amendments Hoses data Ground, Airborne Observed Modeled
What about Wind-Optimal? Wind-optimal is the most efficient trajectory Computationally intensive Availability of wind data Moving target Are great circle routes a good proxy for windoptimal? Compare: Actual Route Wind-Optimal Great Circle (Exclude within 5 miles of airports). For all flights on two sample days Source: J. Bonn
Actual vs. Idealized Trajectories Time Distance 3. 12 Excess time over Wind-Optimal (min) 2.5 2. 1.5 1..5. 22211 2241 Route Great Circle Excess distance over G.C. (nmi) 1 8 6 4 2 22211 2241 Route Wind Optimal DATE DATE When considering improvements to actual routes, In the mean, Great Circles are a good proxy for wind-optimal Potential Benefits Pool: 37, nmi per day Is all of that pool recoverable?
Benefits Pool with Conflicts Use FACET to identify conflicts and provide geometry and aircraft speeds For sample day,.47 conflicts / flight Revised Flight Path Potential Conflict θ Original Flight Path Numerically solve for minimum conflict cost Buffer Cost of Conflict Pool Reduction Adjusted Pool 5 nmi 1.4 nmi 6% 31K nmi/day ($7M/yr) 1 nmi 3.6 nmi 16% 345K nmi/day ($79M/yr) Source: D. Howell, J. Bonn
Excess distance and traffic load Average excess distance per flight (nmi) 1 A Framework to approach En Route Improvements More Directs (URET,PARR,D2) Opportunity Regime More Efficient Routes (RNP, airspace redesign) Route Structure Regime More Capacity or less conflicts (RVSM, URET, Data link) Congestion Regime 2 4 6 8 1 Percent of maximum center traffic
Distance Saved from Lateral Amendments As URET is deployed, we track Number of flight plan amendments Distance savings from lateral amendments Periodically update benefits estimates Free Flight Reports, OMB Exhibit 3 Number of Amendmets per Day URET Amendments 9 8 7 6 5 4 3 2 1 Aug-2 Oct-2 Dec-2 Feb-3 Apr-3 Direct Amendments Source: D. Murphy Jun-3 Aug-3 Oct-3 Dec-3 Other Amendments Distance Saved Per Day (nmi) 2 15 1 5 Distance Saved from Lateral Amendments* 1 2 3 4 5 # of months since IDU *weighted average ZID, ZME ZKC,ZOB,ZAU,ZDC ZJX,ZFW,ZMP
Excess distance vs. traffic load by center Important to establish site-specific baselines ZOA Average excess distance per flight (n. mi.) - has higher traffic levels - handles a higher proportion of arrivals and departures than ZAB 2 15 1 5 More complex route structure Greater susceptibility to capacity-related effects 2 4 6 8 1 Percent of maximum center traffic ZOA ZAB
Efficiency by Phase of Flight Break up flight into segments Track excess distance, flight time, degrees turned Algorithm developed and coded at Free Flight ATALAB generated archive for all flights since 1998 Subset available in ASPM d 1 Origin Airport d 2 ε 2 ε 1 6 n.mi. 4 n.mi. 1 n.mi. d i ε i Mid-point 1 n.mi. 6 n.mi. Destination Airport 4 n.mi. Great Circle Route Actual Flight Track Excess Distance (n. mi) 16 14 12 1 8 6 4 2 Dep to 4 4 to 1 1 to 2 Mid (per 1 mi) 2 to 1 1 to 4 4 to Arr Phase of Flight
En Route Throughput Construct throughput lines ( hoses ) that capture major traffic flows Measure throughput over lines Also track crossing time and position by flight Algorithm developed by Free Flight and OEP Coded at Free Flight ATALAB generated archive for all flights since 1998 1 7 2 19 2 3 4 5 8 9 11 12 18 14 13 6 1 15 16 17
En Route Throughput and Departure Delay Look at impact of holiday flights in ZMA 1 2 3 4 5 7 8 6 9 1 15 2 11 12 18 16 14 13 17 19 Total flights over lines 5 4 3 2 1 En Route Throughput Dec. 26 Dec. 12 6 7 8 9 1 11 12 13 14 15 16 17 18 Local Hour Daily Airport Operations 14 12 1 8 6 4 2 Airport Operations Dec. 12 Dec. 26 FLL MCO MIA PBI TPA Average Departure Delay (min) 4 3 2 1 Average Departure Delay Dec. 12 Dec. 26 FLL MCO MIA PBI TPA +17% -2% -5% +38% -5% +34% +18% +72% +254% +167%
Need for Better En Route Models En Route problems manifest themselves in several ways Excess distance, departure delay, MIT, Ground stops Difficult to separate en route problems from terminal effects Current queuing models have shortcomings Don t deal well with all constraints TRACON capacity No modeling of airspace performance when demand < capacity No Opportunity regime Trajectories are non-adaptive Tactical (Local congestion, weather) Strategic (TFM)
Here s what we d like to see a model do
Modeling Airspace Performance Avg. Excess Distance Excess Distance vs. Sector Load 3 2.5 2 1.5 1.5 2 4 6 8 1 12 14 Percent Load (1 Min Bins) If sector load < capacity, Adjust dwell time stochastically Modeled aircraft approaches new sector If sector load > capacity, Allow to enter and adjust dwell time, OR Delay If delay is excessive, adjust trajectory HOW???
Modeling Airspace Performance How to do route adjustment to avoid excessive delay? Iterative: Limited set of alternate full trajectories Dynamic: Route around congested area Also need ability to implement TFM initiatives if multiple flights are affected
Modeling Airspace Performance What about terminal delay that can t be routed around? Need to deal with airport and TRACON capacity If delay is excessive, may need to implement strategic solution Hold Hold on ground, Then release on same trajectory?
Summary Free Flight uses several en route metrics Projections of future benefits Assessment of deployed tools Our approach Need to understand magnitude of problem (size of pool) Tie projected benefits to observable metrics Establish site-specific metrics baselines Need better en route models
En Route Throughput and Departure Delay 1 2 3 4 5 7 8 6 9 1 15 2 11 12 18 16 14 13 17 19 Total flights over lines 5 4 3 2 1 En Route Throughput Dec. 26 Dec. 12 6 7 8 9 1 11 12 13 14 15 16 17 18 Local Hour Number of hourly departures 2 15 1 5 Airport Operations Dec. 26 Dec. 12 6 7 8 9 1 11 12 13 14 15 16 17 18 Local Hour Average Departure Delay (min) MIA, MCO, TPA 3 25 2 15 1 5 Dec. 26 Dec. 12 Departure Delay 6 7 8 9 1 11 12 13 14 15 16 17 18 Local Hour
Impact of Sector Capacity Use line data to look at excess distance for flights encountering busy sectors 7 6 Encountering a single busy sector seriously affects excess distance Sum of Line XD (nmi) 5 4 3 2 1 to 4 8 to 1 Total Flight Length 5 to 1 over 1 under 5 4 to 8 over 1 Maximum Sector Load (% of sector capacity)
Modeled Sector En Route Daily Delay High Sectors 22 212 Many en route sectors are currently capacity constrained Capacity constraints in en route airspace will become more of a problem in the future
Implementing TRACON capacity Tracon Capacity, Major Airports March 7, 22 Number of AC in Tracon (% of hourly AAR) 7% 6% 5% 4% 3% 2% 1% % max 95th %ile Average of 95 th percentile: 29% ATL BOS BWI CLE CLT CVG DCA DEN DFW DTW EWR FLL IAD IAH JFK LAS LAX LGA MCO MD MIA MSP ORD PDX PHL PHX PIT SAN SEA SFO SLC STL TPA Total daily enroute delay (minutes) Modeled delay with and without Tracon Capacity 2 15 1 5 Unlimited 29% of AAR 22 21 22 Total Daily Enroute Delay (minutes) Modeled delay with and without Tracon capacity 9 8 7 Unlimited 6 5 29% of AAR 4 3 2 1 Baseline 5% 15% 2% 3% Increase in Enroute Sector Capacity