Integrated Analysis of Airport Capacity and Environmental Constraints
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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 • 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 PAGE 2 Overview of Subtasks 4. Analyze Airportal Capacity Constraints 1. Develop Scenarios 2. Develop Metrics 3. Develop List of Critical Airports Runway Constraints Taxiway Constraints Gates Constraints 5. Analyze Airportal Environmental Constraints Fuel Constraints Emissions Constraints Noise Constraints PAGE 3 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 PAGE 4 OEP 35 Airports PAGE 5 FACT 56 Airports PAGE 6 LMI 110 Airports PAGE 7 LMI 310 Airports BLI BLI BLI BLI BLI BLI BFI BFI BFI BFI BFI BFI PAE PAE PAE PAE PAE PAE SEA SEA SEA SEA SEA SEA RNT RNT RNT RNT RNT RNT GEG GEG GEG GEG GEG GEG HIO HIO HIO HIO HIO HIO PSC PSC PSC PSC PSC PSC PDX PDX PDX PDX PDX PDX TTD TTD TTD TTD TTD TTD GTF GTF GTF GTF GTF GTF MSO MSO MSO MSO MSO MSO BTM BTM BTM BTM BTM BTM EUG EUG EUG EUG EUG EUG OR OR OR OR OR OR BOI BOI BOI BOI BOI BOI BZN BZN BZN BZN BZN BZN SJC SJC SJC SJC SJC SJC PIR PIR PIR PIR PIR PIR RAP RAP RAP RAP RAP RAP ATY ATY ATY ATY ATY ATY SLC SLC SLC SLC SLC SLC HWD HWD HWD HWD HWD HWD UT UT UT UT UT UT SMX SMX SMX SMX SMX SMX BFL BFL BFL BFL BFL BFL SAN SAN SAN SAN SAN SAN CDC CDC CDC CDC CDC CDC VGT VGT VGT VGT VGT VGT LAS LAS LAS LAS LAS LAS BUR BUR BUR BUR BUR BUR OXR OXR OXR OXR OXR OXR SNA SNA SNA EGE EGE EGE EGE GJT GJT GJT GJT GJT GJT ASE ASE ASE ASE ASE ASE FAT FAT FAT FAT FAT FAT PSP PSP PSP PSP PSP PSP GCN GCN GCN GCN GCN GCN PRC PRC PRC PRC PRC PRC SEE SEE SEE SEE SEE SEE DVT DVT DVT DVT DVT DVT SDM SDM SDM SDM SDM SDM PHX PHX PHX PHX PHX PHX ABQ ABQ ABQ ABQ ABQ ABQ FFZ FFZ 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PIE PIE PIE LAL LAL LAL LAL LAL PBI PBI PBI PBI PBI SRQ SRQ SRQ SRQ SRQ OPF OPF OPF OPF FXE FXE FXE FXE FXE OPF APF APF APF APF APF APF TMB MIA MIA MIA MIA TMB TMB TMB TMB TMB MIA MFE MFE MFE MFE MFE MFE PAGE 8 PWM PWM PWM PWM PWM BVY BVY BVY BVY BVY BVY BOS BOS BOS BOS BOS BOS ACK ACK ACK ACK ACK ACK “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 PAGE 9 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 P A G E 10 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 P A G E 11 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 P A G E 12 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) P A G E 13 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) P A G E 14 Subtask 4.2: Analyze Airport Capacity Constraints (Taxiways) Taxiway Capacity Model Example: ORD Arrivals Taxiway/Runway Crossing Points Departures P A G E 15 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 P A G E 16 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 P A G E 17 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 airport’s gate capacity 6. Trim arrivals and departures so that airport’s capacity is not violated; decrement baseline aircraft 7. Repeat steps 3 - 6 until arrival denial rate matches baseline percentage reduction P A G E 18 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 P A G E 19 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 P A G E 20 Subtask 5: Analyze Airportal Environmental Constraints Variable Fidelity Terminal Area Modeling Model Environmental Sensitivity Tool NAS-Wide Environmental Screener Regulatory Tools Purpose • • • • • • System Inputs & Assumptions User Results Technology (Underlying Models) Inputs • 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 • • • • • 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 • • • • • • 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 Light-weight Spreadsheet Based Simple Interface Low Fidelity Trend Analysis Results in Secs Heavy-weight Java/C++ Based Simple Interface High Fidelity Policy/Regulatory Analysis • Results in Hours/Days P A G E 21 Subtask 5: Analyze Airportal Environmental Constraints Terminal Area Level 2(NES) Modeling IAD NES 2007 Noise Contour (65/55/45 dB DNL) IAD New Runway EIS 210 Noise Contour (65+ DNL) P A G E 22 Subtask 5: Analyze Airportal Environmental Constraints Terminal Area Level 3 Modeling Legend • Level 3: Regulatory Tools (NASEIM/NIRS) – 12,140 flight tracks – 111 backbones serving 10 runways – Each profile generated to match the existing flow Backbones – ORD Arrivals 30 Day Radar Sample – ORD Arrivals 40 nmi from ORD P A G E 23 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 P A G E 24 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 P A G E 25 Primary and Secondary Constraints at 10 Busiest Airports in 2025 Airport Unconst rained Capacity constraints Runway Taxi Daily Reducti Daily Reducti ops. on ops. on ATL 4,383 3,605 17.8% 3,481 20.6% 4,137 5.6% 4,371 0.3% 4,167 4.9% 3,901 11.0% CLT 2,232 2,232 0.0% 1,987 11.0% 2,076 7.0% 2,148 3.8% 2,108 5.6% 1,896 15.1% DEN 2,621 2,621 0.0% 2,621 0.0% 2,471 5.7% 2,564 2.2% 2,486 5.2% 2,616 0.2% DFW 3,099 3,099 0.0% 3,050 1.6% 3,099 0.0% 3,087 0.4% 2,971 4.1% 2,941 5.1% IAH 2,848 2,810 1.3% 2,848 0.0% 2,752 3.4% 2,639 7.3% 2,609 8.4% 2,697 5.3% LAS 2,760 1,684 39.0% 2,760 0.0% 2,428 12.0% 2,690 2.5% 2,330 15.6% 2,188 20.7% LAX 3,678 2,834 22.9% 3,362 8.6% 2,942 20.0% 3,531 4.0% 3,181 13.5% 2,929 20.4% ORD 4,031 4,031 0.0% 3,892 3.4% 3,391 15.9% 3,979 1.3% 3,903 3.2% 3,829 5.0% PHL 2,518 2,002 20.5% 2,518 0.0% 2,330 7.5% 2,395 4.9% 2,269 9.9% 2,389 5.1% PHX 2,516 2,230 11.4% 2,293 8.9% 2,330 7.4% 2,419 3.9% 2,203 12.4% 2,147 14.7% 30,686 27,148 88.5% 28,812 93.9% 27,956 91.1% 29,823 97.2% 28,227 92.0% 27,533 89.7% Total Gate Daily Reducti ops. on Environmental constraints Fuel NOx Noise Daily Reducti Daily Reducti Daily Reducti ops. on ops. on ops. on Similar tables are created for each of the 310 critical airports for both years P A G E 26 Constraints for the Busiest 10 Airports, 2025 35,000 100% 94% 30,000 89% 97% 91% 90% Gate Noise 92% Operations 25,000 20,000 15,000 10,000 5,000 Unconstrained Runway Taxi Fuel P A G E 27 Nox Constraints for LMI 310 Airports, 2025 180,000 160,000 100% 99% 96% 96% 93% 92% Fuel NOx 87% Daily Operations 140,000 120,000 100,000 80,000 60,000 40,000 20,000 - Unconstrained Runway Taxi Gate Noise P A G E 28 Table Error! No text of specified style in document.-1. Number of Constrained Airports by Category in 2025 Constrained Airports in 2025 Airport Group Busiest10 OEP35 LMI110 LMI310 Constrained Runway Taxi Gate Fuel NOx Noise Primary 3 1 1 0 1 3 Secondary 0 1 1 1 4 4 Total 6 6 9 10 10 10 Primary 4 2 4 3 3 19 Secondary 4 1 0 5 17 7 Total 21 12 27 35 34 35 Primary 5 2 7 21 18 63 Secondary 5 1 7 34 58 9 Total 28 12 79 110 109 103 Primary 5 2 13 111 76 132 Secondary 6 1 10 106 149 18 Total 32 12 95 303 305 237 P A G E 29 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 P A G E 30 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 P A G E 31
