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

Shahab Hasan, Principal Investigator Rosa Oseguera-Lohr, NASA Langley, Technical Monitor

Dou Long, George HartMike Graham, Terry Thompson, Charles Murphy

January 28, 2010

Integrated Analysis of Airport Capacity and Integrated Analysis of Airport Capacity and Environmental ConstraintsEnvironmental Constraints

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 NASA’s research to enable NextGen

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

P A G E 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

P A G E 5

OEP 35 Airports

P A G E 6

FACT 56 Airports

P A G E 7

LMI 110 Airports

P A G E 8

LMI 310 Airports

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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

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

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

P A G E 12


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 13


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 14


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 15


Taxiway Capacity Model Example: ORD

Arrivals

Departures

Taxiway/Runway

Crossing Points

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 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 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

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 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 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

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

P A G E 21

Model Purpose SystemInputs &

Assumptions

User

Inputs

Results Technology(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


• 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.




• Noise Contours

• EDMS

• BADA

• NIRS

• NASEIM

Regulatory Tools• Heavy-weight

• Java/C++ Based


• 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.




• Noise Contours

• EDMS

• BADA

• NIRS

• NASEIM

Subtask 5: Analyze Airportal Environmental Constraints Variable Fidelity Terminal Area Modeling

P A G E 22


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)

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 theexisting flow

Legend

30 Day Radar Sample – ORD Arrivals40 nmi from ORD

Backbones – ORD Arrivals

P A G E 24


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 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

P A G E 26

Primary and Secondary Constraints at 10 Busiest Airports in 2025

Capacity constraints Environmental constraints

Runway Taxi Gate Fuel NOx Noise

Airport Unconstrained

Daily ops.

Reduction

Daily ops.

Reduction

Daily ops.

Reduction

Daily ops.

Reduction

Daily ops.

Reduction

Daily ops.

Reduction

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%

Total 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%

Similar tables are created for each of the 310 critical airports for both years

P A G E 27

Constraints for the Busiest 10 Airports, 2025

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Unconstrained Runway Taxi Gate Noise Fuel Nox

Op

era

tio

ns

100%

89%

94%

91% 90%

97%

92%

P A G E 28

Constraints for LMI 310 Airports, 2025

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

Unconstrained Runway Taxi Gate Noise Fuel NOx

Da

ily O

pe

rati

on

s

100%

92%93%87%

96%99%

96%

P A G E 29

Constrained Airports in 2025

Table Error! No text of specified style in document.-1. Number of Constrained Airports by Category in 2025

Airport Group Constrained Runway Taxi Gate Fuel NOx Noise

Primary 3 1 1 0 1 3

Secondary 0 1 1 1 4 4 Busiest10

Total 6 6 9 10 10 10

Primary 4 2 4 3 3 19

Secondary 4 1 0 5 17 7 OEP35

Total 21 12 27 35 34 35

Primary 5 2 7 21 18 63

Secondary 5 1 7 34 58 9 LMI110

Total 28 12 79 110 109 103

Primary 5 2 13 111 76 132

Secondary 6 1 10 106 149 18 LMI310

Total 32 12 95 303 305 237

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

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

Documents

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