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Shahab Hasan, Principal Investigator Rosa Oseguera-Lohr, NASA Langley, Technical Monitor Dou Long, George Hart Mike Graham, Terry Thompson, Charles Murphy.

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Presentation on theme: "Shahab Hasan, Principal Investigator Rosa Oseguera-Lohr, NASA Langley, Technical Monitor Dou Long, George Hart Mike Graham, Terry Thompson, Charles Murphy."— Presentation transcript:

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

2 P A G E 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 NASAs research to enable NextGen

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

4 P A G E 4 Overview of Subtasks 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 P A G E 5 OEP 35 Airports

6 P A G E 6 FACT 56 Airports

7 P A G E 7 LMI 110 Airports

8 P A G E 8 LMI 310 Airports

9 P A G E 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 P A G E 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 P A G E 11 Subtask 4.2: Analyze Airport Capacity Constraints (Taxiways) Methodology Three-pronged approach for taxiway constraint analysis: 1.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 2.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 3.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 P A G E 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: 1.Airport has only 1 active runway and that all operations take place on this runway 2.All traffic must taxi across this runway at a single crossing point in order to takeoff or arrive at the terminal 3.Each runway operation requires the closing of the runway and runway crossing for 60 seconds 4.Each runway crossing takes 30 seconds

13 P A G E 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 FAAs 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 P A G E 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 FAAs 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 P A G E 15 Subtask 4.2: Analyze Airport Capacity Constraints (Taxiways) Taxiway Capacity Model Example: ORD Arrivals Departures Taxiway/Runway Crossing Points

16 P A G E 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 airports gate availability over time using –Gate Capacity: the airports 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 P A G E 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 airports 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 P A G E 18 Subtask 4.3: Analyze Airport Capacity Constraints (Gates) Model Execution 1.Calculate the reference number of aircraft at the gates 2.Build an airport-by-airport, epoch-by-epoch schedule of arrivals and departures 3.Cycle through each 15-minute epoch, creating a running count of the change in the number of aircraft at the gate 4.Add these net change values to the baseline value to provide the total aircraft at the gates throughout the day 5.Compare these values to the airports gate capacity 6.Trim arrivals and departures so that airports capacity is not violated; decrement baseline aircraft 7.Repeat steps until arrival denial rate matches baseline percentage reduction

19 P A G E 19 Overview of Subtask 5 Analyze 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 P A G E 20 Subtask 5: Analyze Airportal Environmental Constraints Environmental 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 P A G E 21 ModelPurposeSystem Inputs & Assumptions User Inputs ResultsTechnology (Underlying Models) Environmental Sensitivity Tool Light-weight Spreadsheet Based Simple Interface Low Fidelity Trend Analysis Results in Secs ICAO/EDMS times-in-mode for fuel and emissions ICAO distance based fuel burn matrix AEM Noise Coefficients Population density by airport based on 2000 US Census. Day/Night distribution Schedule of operations (origin, destination, aircraft, departure time) Fuel per flight divided by mixing height. Emissions per flight Population exposed to noise for 55 & 65 dBA DNL. EDMS BADA AEM NAS-Wide Environmental Screener Light-weight Java Based Simple Interface Medium Fidelity Policy/Trend Analysis Results in Mins US 2000 Census Flight performance database of all aircraft times-in-mode based on stage-length Great-circle distance fuel burn Noise maps database for all aircraft Schedule of operations (origin, destination, aircraft, departure time, arrival time) Runway configuration and use. Fuel per flight divided by mixing height. Emissions per flight Population exposed to noise for 55 & 65 dBA DNL. Noise Contours EDMS BADA NIRS NASEIM Regulatory Tools Heavy-weight Java/C++ Based Simple Interface High Fidelity Policy/Regulatory Analysis Results in Hours/Days US 2000 Census EDMS (AEDT) fuel and emissions below 3K BADA based fuel above 3K SAE based aircraft performance for noise Schedule of operations assigned to trajectories. Simple one to one trajectory or detailed backbones. Fuel per flight divided by mixing height. Emissions per flight Population exposed to noise for 55 & 65 dBA DNL. Noise Contours EDMS BADA NIRS NASEIM Subtask 5: Analyze Airportal Environmental Constraints Variable Fidelity Terminal Area Modeling

22 P A G E 22 Subtask 5: Analyze Airportal Environmental Constraints Terminal Area Level 2(NES) Modeling IAD New Runway EIS 210 Noise Contour (65+ DNL) IAD NES 2007 Noise Contour (65/55/45 dB DNL)

23 P A G E 23 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 the existing flow Legend 30 Day Radar Sample – ORD Arrivals 40 nmi from ORD Backbones – ORD Arrivals

24 P A G E 24 Subtask 5: Analyze Airportal Environmental Constraints Airports 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 P A G E 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 P A G E 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 P A G E 27 Constraints for the Busiest 10 Airports, 2025

28 P A G E 28 Constraints for LMI 310 Airports, 2025

29 P A G E 29 Constrained Airports in 2025

30 P A G E 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 P A G E 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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