Have Descents Really Become More Efficient? Presented by: Dan Howell and Rob Dean Date: 6/29/2017
Outline Introduction Airport Initiative Categories Methodology Results Comparison with NextGen Performance Scorecard Conclusion and Next Steps 2
Introduction: Purpose A common method for determining the magnitude of the opportunity to improve ATM service is to estimate a benefits pool. The purpose of this study is to use benefits pool metrics to examine whether historical initiatives have had an impact. A similar methodology has been used in other ATM studies This study focuses on using the benefits pool methodology to gauge the success of Performance Based Navigation (PBN) initiatives in the US from 2015 Comparison of Air Traffic Management- Related Operational Performance: U.S./Europe 3
Introduction: TBO and PBN One of the cornerstones of NextGen is delivering improved services through Trajectory Based Operations (TBO) Procedural Control Control on where we think the aircraft is Surveillance Based Control Control on where we know the aircraft is Trajectory Based Control Control on where we know the aircraft will be The backbone of precise trajectory operations is Performance Based Navigation (PBN) currently being implemented as RNAV and RNP routes and procedures including Optimized Profile Descents (OPDs). 4
Introduction: Problem PBN cannot be used to full potential in the current environment because 60,000 daily flights are competing for the same resources (airspace and airports). Solution: a common schedule for controllers and tools to ensure that schedule is met Challenge: develop a system that provides a common schedule to controllers but is flexible enough to deal with changes dynamically NextGen defines a portfolio of capabilities (not just one tool) referred to as Time-Based Flow Management (TBFM) Part of the vision for TBFM is to enable improvements in both fuel and throughput efficiencies by applying spacing only where needed 5
Introduction: TBFM Use Why isn t TBFM used all the time consistently across the National Airspace System? The system is evolving at the same time as PBN, surveillance (ADS- B), and data sharing capabilities (DataComm, SWIM) The goal of TBFM is to apply spacing only when needed. Some places just don t need it while other places aren t ready for it. The current tools may not be flexible enough to meet the goal in all situations (weather, etc.). Ensuring flexibility so that we don t make things worse through an overly rigid schedule is a major concern The TBFM Portfolio contains an evolving set of capabilities to accomplish the vision 6
Introduction: TBFM Portfolio Tool Airborne Metering through Traffic Management Advisor (TMA) Arrival management/situational Awareness Departure Scheduling En route Departure Capability (EDC) Implementing TMA at Additional airports Implement Adjacent Center Metering (ACM) at additional locations TBFM Information Sharing Using RNAV trajectories to conduct TBM Integrated Departure/Arrival Capability (IDAC) initial Ground-Based Interval Management - Spacing (GIM-S) (Extended Metering and Speed Advisory) Implementing IDAC at Additional airports TBM in Terminal Environment/Terminal Sequencing and Spacing(TSAS) Interval Management - Spacing (IM-S) (Arrivals & Approach and Cruise) Flight Operations Center (FOC) Preferences Incorporated into Metering Lateral Maneuvering for Delay Absorption (Path Stretch) Complex Clearances Years Pre FY2010 FY2010- FY2015 FY2014+ FY2018 FY2019 FY2022 FY2022 FY2022+ FY2023+ 7
Airport Initiative Categories While TBFM is available at all ARTCCs, not all the airports use airborne metering to the TRACON MITRE TBFM Performance Summary Dashboard used to examine airports using metering to the TRACON in at least half of FY2015 Availability of OPDs at specific airports was taken from the NextGen Performance Snapshots website: www.faa.gov/nextgen/snapshots/ In the results we group airports in following categories: No OPDs or Metering Metering only OPDs only OPDs and Metering to TRACON Airport OPDs Metering to TRACON Airport OPDs Metering to TRACON ATL LGA BOS MCO BWI MDW CLT MEM DCA MIA DEN MSP DFW ORD DTW PHL EWR PHX FLL SAN IAD SEA IAH SFO JFK SLC LAS TPA LAX 8
Methodology Days Chosen Many FAA studies use a set of representative days to model impacts over a year The set is chosen so that when extrapolated to a year the NAS-wide demand, capacity, weather, and delay is very close to full-year metrics The analysis used Traffic Flow Management System (TFMS) trajectory data from the representative days in 2 years: 2010 (12 days) 2015 (16 days) FY2010 FY2015 10/6/2009 11/18/2014 10/17/2009 12/13/2014 11/20/2009 12/16/2014 1/10/2010 12/26/2014 3/9/2010 1/11/2015 3/25/2010 1/24/2015 5/6/2010 3/6/2015 5/18/2010 3/19/2015 6/5/2010 4/25/2015 7/3/2010 5/12/2015 7/13/2010 6/2/2015 7/22/2010 6/14/2015 7/7/2015 7/16/2015 7/19/2015 8/31/2015 9
Methodology Savings Calculations Potential Fuel and Time Savings was performed in a similar manner to many previous studies Examined flights from 100 NM to runway Identified level segments (consecutive altitude reports that differed by 300 feet or less) Computed how long each flight would have spent at cruise if it had followed an optimized profile descent Used BADA 3.13 performance tables to estimate fuel burn and time of actual level segment compared to fuel burn and time of level segment if flown at cruise Many flights had to be filtered because of data quality issues 50% of the 2010 data was removed and 30% of the 2015 data was removed After filtering, still had a minimum of 2000 flights for each year and each airport 10
Methodology Most common aircraft One factor that impacts potential fuel burn and flight time savings is the fleet mix at an airport To account for fleet mix changes between the years we also examined the savings metrics for the most common aircraft type at each airport over both 2010 and 2015. This method limited the amount of flights examined considerably Airport Aircraft type Airport Aircraft type Airport Aircraft type ATL MD88 IAD A320 MSP A320 BOS A320 IAH B738 ORD A320 BWI B737 JFK A320 PHL A319 CLT A321 LAS B737 PHX B737 DCA E170 LAX B738 SAN B737 DEN B737 LGA A320 SEA B738 DFW MD82 MCO B737 SFO A320 DTW CRJ2 MDW B737 SLC A320 EWR B738 MEM DC10 TPA B737 FLL A320 MIA B738 11
Methodology - Congestion Likely the most important constraint to enabling the use of OPDs beyond design of the procedure itself is demand congestion. Congestion defined as the ratio of arrival demand to arrival capacity The arrival demand was calculated per aircraft by defining the arrival queue as the number of aircraft that land between the time when an aircraft enters the study and when it lands. Potential Fuel Savings (gal) vs Arrival Queue Size at MDW and MEM 12
Methodology - Congestion For Capacity, we used the maximum Airport Acceptance Rate (AAR) at each airport from the Aviation System Performance Metrics database Congestion = Arrivals per hour/ AAR = x% Aggregated levels of congestion based on a 2007 FAA benefits estimate* OPD requirement Congestion regime Congestion level No automation necessary <40% Low Some automation needed 40% and <70% Medium Advanced automation and aircraft tools 70% High * FAA, Benefits Basis of Estimate for Surveillance and Broadcast Services Program, August 2007 13
Results (all aircraft) For each airport and year, a mean potential fuel reduction and time savings was calculated. (Trimmed top 1% to limit impact of outliers) Results are presented as a percent reduction in either the mean vertical potential fuel savings or the mean vertical potential time savings between 2010 and 2015 at each of the selected airports. The percent reduction is used instead of the raw difference to normalize across differences between airports in terminal airspace, fleet mix, and procedures. A positive reduction indicates that the benefit pool has decreased in magnitude, and consequently, vertical efficiency has increased. The results are presented by airport and then grouped by initiative using the mean of the individual airport results. 14
Results (all aircraft) Percent reduction of potential savings A positive value indicates that the vertical efficiency has increased between 2010 and 2015 Negative values indicate that vertical efficiency has decreased between the two years Colors represent initiative groupings Quite a bit of variability within each group Potential Fuel Savings Reduction (all) Potential Time Savings Reduction (all) 15
Results (all aircraft) continued Initiative grouping Potential Fuel savings Potential Time savings Mean % reduction Mean % reduction No OPDs or Metering -4.4% -10.4% Metering only 14.6% 8.5% OPDs only 6.9% 11.0% OPDs and Metering to TRACON 37.6% 32.6% The OPDs and Metering to TRACON grouping shows a much higher reduction in both mean fuel savings and time savings, compared to the other three groupings, implying an increase in vertical efficiency. The OPDs only grouping and the Metering only grouping have a higher reduction than the No OPDs or metering grouping. 16
Results (most common aircraft) Repeated exercise using only the most common aircraft type Observations at airport level Fleet mix changes do make a difference at some airports DTW, IAD, and MEM have more positive results Large negative result at TPA seen in previous charts seems to lessen OPDs only airports show a more positive trend than in all aircraft charts Potential Fuel Savings Reduction (most common) Potential Time Savings Reduction (most common) 17
Results (most common aircraft) cntnd. Initiative grouping Potential Fuel savings Mean % reduction Potential Time savings Mean % reduction No OPDs or Metering 6.9% -3.1% Metering only 12.3% 11.3% OPDs only 16.4% 12.7% OPDs and Metering to TRACON 35.9% 35.9% Individual airport results differ between using all aircraft and the most common aircraft type indicating that the fleet mix changes are significant However, the summary between initiative groupings tells a similar story 18
Results (congestion) Expected trends: Low congestion level - vertical efficiency would increase the most at airports with OPDs as opposed those without. Medium and high congestion levels - vertical efficiency would depend on having both OPDs and metering, so expect to see a drop in efficiency for airports without metering Actual results: OPDs and Metering to the TRACON shows a significant increase in vertical efficiency across all congestion levels OPDs only and Metering only show significantly larger increases in vertical efficiency in low congestion and both decrease as congestion increases No OPDs or Metering grouping appears to rise with higher levels of congestion 19
Comparison to NextGen scorecard As a separate check of the conclusions, a similar analysis was performed on different, but related metrics from the NextGen Performance Scorecard website: Time in Level Flight Number of Level-offs Metrics were only available after 2011 so reduction is from 2011 to 2015 Time in Level Flight Reduction Number of Level-offs Reduction 20
Comparison to NextGen scorecard cntnd. Initiative grouping Distance in level flight Mean % reduction Number of level offs Mean % reduction No OPDs or Metering -5.0% 0.2% Metering only 2.1% -0.9% OPDs only 8.1% 8.4% OPDs and Metering to TRACON 17.9% 15.7% The NextGen Performance results show similar trends as compared to the reduction in the benefits pools. The magnitude of the NextGen Scorecard reductions tend to be less than found in the benefits pools analysis. The Scorecard results also show more noticeable difference between the OPDs only and the Metering only groupings than the benefits pools results. 21
Conclusion and Next Steps Conclusion Yes, descents at FAA airports with procedures and automation to enable them have become more vertically efficient Results suggest procedures plus time-based metering of arrivals to the TRACON enables more vertically efficient descents than procedures or metering alone Next Steps Examine impact on lateral as well as vertical efficiency Likely need arrival fix, runway use, wind data to calculate properly Examine other factors Geometry of airspace, Mix of arrival gates used, Equipage, Weather, Procedure design effectiveness More nuanced method to account for changing fleet mix Segregate impacts of specific procedures or TBFM capabilities Example: largest increase in vertical efficiency was experienced at PHX; this airport was also the only one using speed advisories produced by the GIM-S decision support tool to reduce vectoring and increase meter fix crossing time accuracy during 2015. Such analyses could substantiate expansion of current TBFM portfolio tools and provide justification for future tools such as TSAS, Path Stretch, and Advanced Interval Management. 22