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Integrated Analysis of Airport Capacity and Environmental Constraints

Integrated Analysis of Airport Capacity and Environmental Constraints. January 28, 2010. Shahab Hasan, Principal Investigator Rosa Oseguera-Lohr, NASA Langley, Technical Monitor. Dou Long, George Hart Mike Graham, Terry Thompson, Charles Murphy. Task Objective.

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Integrated Analysis of Airport Capacity and Environmental Constraints

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  1. Integrated Analysis of Airport Capacity and Environmental Constraints January 28, 2010 Shahab Hasan, Principal Investigator Rosa Oseguera-Lohr, NASA Langley, Technical Monitor Dou Long, George HartMike Graham, Terry Thompson, Charles Murphy

  2. Task Objective • Identify and rank key factors limiting the achievement of NextGen goals • Identify capabilities required and gaps in available tools for conducting system-level trade and benefit studies • Results will help prioritize NASA’s research to enable NextGen

  3. Overview of Subtasks 4. Analyze Airportal Capacity Constraints 1. Develop Scenarios Runway Constraints Taxiway Constraints Gates Constraints 2. Develop Metrics 5. Analyze Airportal Environmental Constraints 3. Develop List of Critical Airports Fuel Constraints Emissions Constraints Noise Constraints

  4. Overview of Subtasks 1 - 3 • Subtask 1: Develop Set of Scenarios • 2015 and 2025 flight schedules, generated by FAA, used by JPDO • NextGen capacities developed and used by JPDO • Subtask 2: Develop Set of Metrics • Throughput is our primary metric • Delay is also used for assessing the robustness of future operations • Subtask 3: Develop Set of Critical Airports • 110 large airports with capacities used in prior LMI analyses plus 200 additional airports with capacities developed by the team • The next largest airports from NPIAS with consideration of infrastructure, location relative to major metropolitan area or airport, and traffic mix • Total of 310 airports • 98.6% of air carrier operations, 99.8% of air carrier enplanements

  5. OEP 35 Airports

  6. FACT 56 Airports

  7. LMI 110 Airports

  8. LMI 310 Airports

  9. “One-Off” Constraint Analysis Methodology • Estimate the effect of one constraint by assuming there is no other constraint, at each of the critical airports • Capacity constraints • Runway capacity • Gate capacity • Taxi capacity • Environmental constraints • Fuel burn targets • Local NOx targets • Noise targets • Method: Trim flights from the unconstrained demand schedule to satisfy the constraint

  10. Subtask 4.1: Analyze Airport Capacity Constraints (Runways)Runway Capacity Analysis at 310 Critical Airports • We assume no change to the airport capacities at the smaller 200 airports • Likely cost prohibitive for NextGen deployment • For the 110 larger airports, their capacities can be increased by • New runways • NextGen technologies • One primary airport runway configuration for each meteorological operating condition • Airport runway configurations based on analysis of FACT2 and FAA configurations, airport diagrams, capacity data, procedure charts, and knowledge from prior tasks

  11. Subtask 4.2: Analyze Airport Capacity Constraints (Taxiways)Methodology • Three-pronged approach for taxiway constraint analysis: • Airport Elimination – establish a conservative lower bound for taxi capacities at 310 critical airports • It is very difficult to determine the exact taxiway capacity for a given airport – by establishing a lower bound for taxiway capacity and comparing it to peak demand, we can determine with confidence whether the airport will be taxi-constrained • Configuration Analysis – determine if airports are unlikely to have taxi capacity shortages based on their layout and configuration • Taxi capacity can be determined not to be a constraint if the airport is laid out or operated in such a way that runway/taxiway interaction is minimal • Event simulation models at most of the OEP 35 airports • Simulation is well-suited to modeling the complex surface interactions between aircraft, however building simulations for all 310 airports would be too time consuming for this task

  12. Subtask 4.2: Analyze Airport Capacity Constraints (Taxiways)Approach 1: Airport Elimination Method • Goal: determine those airports whose demand levels are so low that they will never encounter delays due to taxiway constraints • Approach: transform each airport into an abstracted generic inefficient airport by making the following assumptions: • Airport has only 1 active runway and that all operations take place on this runway • All traffic must taxi across this runway at a single crossing point in order to takeoff or arrive at the terminal • Each runway operation requires the closing of the runway and runway crossing for 60 seconds • Each runway crossing takes 30 seconds

  13. Subtask 4.2: Analyze Airport Capacity Constraints (Taxiways)Approach 2: Configuration Analysis • Taxiway delay is believed to be caused by interaction between the taxiways and the runways • Therefore, if an airport consistently operates under a configuration (at least 60% of the time) that does not include this interaction, taxiway delay at the airport will be minimal • We used airport configuration data from the FAA’s 2004 Airport Capacity Benchmark study and from ASPM (limited to the 77 airports covered by ASPM) • All of the OEP 35 airports were either eliminated using this approach or simulated explicitly (Approach 3, next slide)

  14. Subtask 4.2: Analyze Airport Capacity Constraints (Taxiways)Approach 3: Simulation of Taxi Operations • Arena simulation models for 20 of the OEP 35 Airports • ATL, BOS, CLE, CLT, CVG, DCA, DFW, EWR, HNL, LAS, LAX, LGA, MCO, MDW, ORD, PHX, SAN, SEA, SLC, and STL • Airports modeled using their most common configuration according to FAA’s 2004 Airport Capacity Benchmark • Models differentiate between delay caused by runway congestion and delay caused by taxiway congestion • Simulations use an iterative approach, trimming flights when delays exceed tolerances (individual flight delay > 15 mins)

  15. Subtask 4.2: Analyze Airport Capacity Constraints (Taxiways)Taxiway Capacity Model Example: ORD Arrivals Taxiway/Runway Crossing Points Departures

  16. Subtask 4.3: Analyze Airport Capacity Constraints (Gates)Gate Capacity Model Summary • LMI developed a new, Java-based model to model gate capacity and demand • Model execution time is less than 10 minutes • Calculate each airport’s gate availability over time using • Gate Capacity: the airport’s total number of gates • Gate Demand: a schedule of arrivals and departures of aircraft requiring gate access • Reference Point: a known number of aircraft at the gates at some point in time • The model focuses on gates with passenger bridges • The model analyzes all 310 airports, identifies those that are gate constrained, and determines what percentage of flights that would need to be trimmed in order for the airport to remain under capacity

  17. Subtask 4.3: Analyze Airport Capacity Constraints (Gates)Model Execution: Trimming Flights • Flight trimming takes place between 5:30 AM and 11:00 PM local time. • Flights arriving outside of this window are not subject to gate constraints • This policy is designed to account for airports’ practice of shuffling aircraft off the gates and into remain-overnight parking areas when gate space is limited • If gate capacity is exceeded, we create an alternative arrival schedule: • Any arrival that would bring the total number of aircraft on the ground over the airport’s limit is trimmed from the schedule • A corresponding future departure is also removed from the departure schedule • We record the total number of arrivals trimmed, as well as the resulting arrival acceptance rate

  18. Subtask 4.3: Analyze Airport Capacity Constraints (Gates)Model Execution • Calculate the reference number of aircraft at the gates • Build an airport-by-airport, epoch-by-epoch schedule of arrivals and departures • Cycle through each 15-minute epoch, creating a running count of the change in the number of aircraft at the gate • Add these net change values to the baseline value to provide the total aircraft at the gates throughout the day • Compare these values to the airport’s gate capacity • Trim arrivals and departures so that airport’s capacity is not violated; decrement baseline aircraft • Repeat steps 3 - 6 until arrival denial rate matches baseline percentage reduction

  19. Overview of Subtask 5Analyze Airportal Environmental Constraints • Fuel constraint analysis • Analyze/trim flights at all 310 airports based on the current JPDO fuel efficiency metrics • Use the current JPDO goal of 1% improvement per year compounded annually to define the future fuel efficiency targets • Emissions constraint analysis • Analyze/trim flights at all 310 airports using the production of NOx as the metric • Apply the fuel efficiency goal to NOX as well, 1% improvement per year compounded annually to define the future targets • Noise constraint analysis • Analyze/trim flights at all 310 airports based on the current JPDO noise metrics of population exposed to 65 dB DNL • Use the current JPDO goal of 4% improvement per year compounded annually to define the future noise targets

  20. Subtask 5: Analyze Airportal Environmental ConstraintsEnvironmental Methods Considered • Level 1: Schedule Based • Noise/Fuel/Emissions calculations are based solely on flight schedules, no track data used • Level 2: Simplified Flight Tracks • Noise/Fuel/Emissions are based on straight in/out flights tracks and schedules along with runway use • Level 3: Radar Based • Noise/Fuel/Emissions are based on a radar sample of actual radar track data and known flight schedules

  21. Subtask 5: Analyze Airportal Environmental ConstraintsVariable Fidelity Terminal Area Modeling

  22. Subtask 5: Analyze Airportal Environmental ConstraintsTerminal Area Level 2(NES) Modeling IAD NES 2007 Noise Contour (65/55/45 dB DNL) IAD New Runway EIS 210 Noise Contour (65+ DNL)

  23. Legend Backbones – ORD Arrivals 30 Day Radar Sample – ORD Arrivals 40 nmi from ORD Subtask 5: Analyze Airportal Environmental Constraints Terminal Area Level 3 Modeling • Level 3: Regulatory Tools (NASEIM/NIRS) • 12,140 flight tracks • 111 backbones serving 10 runways • Each profile generated to match theexisting flow

  24. Subtask 5: Analyze Airportal Environmental ConstraintsAirports Environmental Analysis Input • For the level 2 modeling we developed lower fidelity terminal areas based on runway configuration and weather data for all 310 airports. • For the level 3 modeling we developed higher fidelity radar driven terminal areas inputs for the FACT 56 airports. • Used two sources (ATA-LAB or PDARS) • Updates to the OEP Airports • New runways - ATL, BOS, CVG, LAX, MSP, STL • Runway extensions – PHL • Generation of the terminal areas for the additional 21 • ABQ, AUS, BDL, BHM, BUR, GYY, HOU, HPN, ISP, LGB, MKE, OAK, ONT, PBI, PVD, RFD, SAT, SJC, SNA, SWF, TUS

  25. Results • At each of the 310 critical airports • Projected throughput under each constraint • Primary and secondary constraints • Aggregate results • by group: busiest 10, OEP 35, LMI 110, and LMI 310 • and by constraint • Capacity: runway, taxiway, and gates • Environmental: emission, NOx, and noise • and by year: 2015 and 2025

  26. Primary and Secondary Constraints at 10 Busiest Airports in 2025 Similar tables are created for each of the 310 critical airports for both years

  27. Constraints for the Busiest 10 Airports, 2025

  28. Constraints for LMI 310 Airports, 2025

  29. Constrained Airports in 2025

  30. Conclusions • Even with full NextGen implementation, some constraints will still exist at some airports • The overall system projected throughput will be no more than the worst constrained case, losing about 15% of total operations in 2025 (310 airport case under noise) • Runway constraints are more binding for the largest airports (top 10), losing about 11% operations • Environmental constraints are widespread and noise is most binding • The environmental goals are quite aggressive and directly affect the results of this study

  31. Caveats and Limitations • Decomposing the system constraints is an analytical technique; we recognize that in the real world, everything is interconnected and mostly inseparable • Demand forecasts are ever-changing and never perfect; the analysis necessarily is a snapshot • Capacity estimates are analytically rigorous and our assumptions are reasonable and clearly documented; however, fully successful and timely R&D and implementation of capacity enhancements is an optimistic assumption • The projected throughput metric, while very useful, models an extreme response (flight trimming) and, in this analysis, we did not model other likely operator responses such as schedule smoothing and use of secondary airports

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