'HFember 2011 

NASA/CR—2011-217302

A Concept for Flexible Operations and 

Optimized Traffic into Metroplex Regions 

Daniel DeLaurentis, Steve Landry, and Dengfeng Sun 

Purdue University, West Lafayette, Indiana 

Fred Wieland and Ankit Tyagi 

Intelligent Automation, Inc., Rockville, Maryland
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'HFember 2011 

NASA/CR—2011-217302

A Concept for Flexible Operations and 

Optimized Traffic into Metroplex Regions 

Daniel DeLaurentis, Steve Landry, and Dengfeng Sun 

Purdue University, West Lafayette, Indiana 

Fred Wieland and Ankit Tyagi 

Intelligent Automation, Inc., Rockville, Maryland 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802

Acknowledgments 

The authors acknowledge, first and foremost, the sponsorship of this research by the National Aeronautics 

and Space Administration under contract NNL10AA15C. In particular, the input and coordination of 

project Technical Monitor, Michael Sorokach, and Metroplex Lead Scientist, Rosa Oseguera-Lohr (both 

from the NASA Langley Research Center) were of tremendous value to our research team. Finally, the 

authors acknowledge the significant efforts of the dedicated team of graduate students at Purdue 

University without whom much of the progress reported in this document would not have been possible: 

Seung Yeob (Simon) Han, Yiwu Jian, Taehoon Kim, Vishnu Vinay, and Peng Wei. We sincerely thank 

them all and hope their education was enhanced by this project experience. 

The use of trademarks or names of manufacturers in this report is for accurate reporting and does not 

constitute an official endorsement, either expressed or implied, of such products or manufacturers by the 

National Aeronautics and Space Administration. 
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

iii

Table of Contents 

1. Introduction ........................................................................................................................................................... 1

1.1. Context: Delays at Metroplexes ................................................................................................................... 1

1.2. Concept Summary and Objectives of the Study ........................................................................................... 2

2. Flexible Operations Concept ................................................................................................................................. 3

2.1. Operator CONOPs ....................................................................................................................................... 3

2.2. Potential Passenger CONOPs....................................................................................................................... 4

3. Potential Benefits and Barriers of Flexible Operations Concept ........................................................................... 6

3.1. Potential Benefits ......................................................................................................................................... 6

3.2. Potential Barriers .......................................................................................................................................... 8

4. Technical Approach ............................................................................................................................................... 8

4.1. Model Overview .......................................................................................................................................... 8

4.2. Flexible Flights Selection (FFS) .................................................................................................................. 9

4.2.1. Overview of FFS Model .......................................................................................................................... 9

4.2.2. Criteria for Flexible Operations ............................................................................................................. 10

4.2.3. Data Sources .......................................................................................................................................... 10

4.2.4. How Many Passengers Connect? ........................................................................................................... 11

4.2.5. Development of FFS Model based on the Number of Connecting Passengers ...................................... 11

4.2.6. Simple Example of Selecting Flexible Flights using FFS model ........................................................... 13

4.2.7. Application of FFS Model to N90 and SCT Metroplex ......................................................................... 13

4.2.8. Factors addressed by the FFS model ..................................................................................................... 15

4.3. Linear Time-Varying (LTV) Optimizer ..................................................................................................... 16

4.3.1. LTV Overview ....................................................................................................................................... 16

4.3.2. Virtualization for N90 and SCT Metroplexes ........................................................................................ 18

4.3.3. Data inputs to LTV ................................................................................................................................ 22

4.3.4. Estimation of all possible routes of a flexible flight .............................................................................. 25

4.3.5. Final LTV output data sets ..................................................................................................................... 27

4.4. Multi-center Traffic Management Advisor Model (McTMA) ................................................................... 28

4.4.1. Airport-runway balancer (ARB) ............................................................................................................ 29

4.5. How the models were linked ...................................................................................................................... 31

4.6. Airspace Concepts Evaluation System (ACES) ......................................................................................... 32

4.7. Demand / Business Model ......................................................................................................................... 34

4.7.1. Introduction ........................................................................................................................................... 34

4.7.2. Airline Origin and Destination Survey (DB1B) .................................................................................... 34

4.7.3. Combining DB1B Database Tables ....................................................................................................... 34

4.7.4. DB1B Data Interpretation ...................................................................................................................... 35

4.7.5. On-Time performance data .................................................................................................................... 37
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4.7.6. Metroplex Markets ................................................................................................................................. 39

(Note: Equations are numbered in this sections 4.7.6 – 4.7.8 since there are several) ........................................ 39

4.7.7. Metroplex Airline Models ..................................................................................................................... 42

4.7.8. Demand / Business model ...................................................................................................................... 46

5. Results ................................................................................................................................................................. 50

5.1. Results from ACES Experiments (N90 and SCT, On-demand/GA and Commercial) ............................... 50

5.1.1. Introduction ........................................................................................................................................... 50

5.1.2. Air Taxi and GA Flight Analysis ........................................................................................................... 51

5.1.3. ACES Smart Case Analysis ................................................................................................................... 57

5.1.4. Commercial Only Smart Case Analysis................................................................................................. 61

5.1.5. Commercial only distinct N90 and SCT Analysis ................................................................................. 63

5.2. Results from LTV – McTMA model ......................................................................................................... 70

5.2.1. Scheduling results .................................................................................................................................. 73

5.2.2. Cost of delay for N90 ............................................................................................................................ 77

5.2.3. Scheduling Results on SCT Metroplex data .......................................................................................... 79

5.3. Best Case Results from a Simple Flight Allocation ................................................................................... 82

5.4. Comparison of results between all cases .................................................................................................... 83

5.5. Intermodal study results ............................................................................................................................. 85

6. Conclusions ......................................................................................................................................................... 90

7. One Potential Future Work Item .......................................................................................................................... 91

8. References and Citations ..................................................................................................................................... 93

9. Appendix A.......................................................................................................................................................... 95

 
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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LIST OF FIGURES 

Figure 1: Example snapshot of flights and runway usage at three N90 airports ............................................................ 2

Figure 2: Comparison of New York-New Jersey Metroplex (N90) hourly maximum capacity between theoretical, 

observed, and new concepts. [Theoretical and observed maximums derived from Aviation System Performance 

Metrics (ASPM) data]. .................................................................................................................................................. 3

Figure 3: On left: Flight plan with fixed destination airport, as done today; On right: Flight plan with metroplex 

destination...................................................................................................................................................................... 4

Figure 4: Snapshot of current Expedia® interface with destination listed as an airport ................................................ 5

Figure 5: Altered interface with metroplex-bound option included ............................................................................... 5

Figure 6: Overview of the integrated simulation model ................................................................................................ 9

Figure 7: Two processes for FFS model. ‘MP’ = Metroplex ....................................................................................... 12

Figure 8: Distribution of FFS’s outputs from histogram approach for Q3 2008 flights arriving at JFK ..................... 12

Figure 9: Notional selection of flexible flights using FFS model ................................................................................ 13

Figure 10: Distribution of flights arriving at N90 big-three airports on November 7th in 2008.................................. 13

Figure 11: Distribution of FFS-selected candidate flights arriving at N90 big-three airports ..................................... 14

Figure 12: Distribution among flights NOT candidates for flexible operations .......................................................... 15

Figure 13: Distributions of FFS-selected candidate flights (Left) and flights NOT candidates for flexible operations 

(Right) for SCT metroplex airports ............................................................................................................................. 15

Figure 14: Traffic flow pattern of N90-bound ............................................................................................................. 18

Figure 15: Traffic flow pattern of N90-bound zoom in 1 ............................................................................................ 19

Figure 16: Traffic flow pattern of N90-bound zoom in 2 ............................................................................................ 19

Figure 17: Traffic flow pattern of SCT-bound ............................................................................................................ 20

Figure 18: Traffic flow pattern of SCT-bound zoom in 1 ............................................................................................ 20

Figure 19: Traffic flow pattern of SCT-bound zoom in 2 ............................................................................................ 21

Figure 20: Traffic flow pattern of N90 and SCT ......................................................................................................... 21

Figure 21: Obtaining graph edges from jet route, including intersection points .......................................................... 22

Figure 22: Hash table indexed grids with waypoints ................................................................................................... 23

Figure 23: The original and optimized routes for a flexible flight ............................................................................... 25

Figure 24: Original Routes of B747 to JFK and EWR ................................................................................................ 26

Figure 25: Virtual route of not existing city pairs ........................................................................................................ 27

Figure 26: The example of output format of LTV ....................................................................................................... 27

Figure 27: Data flow diagram of the entire system ...................................................................................................... 28

Figure 28: Multi-center Traffic Management Advisor model schematic .................................................................... 29

Figure 29: ARB example ............................................................................................................................................. 31

Figure 30: ACES/LTV/McTMA model interactions ................................................................................................... 31

Figure 31: High Level System Diagram of ACES ....................................................................................................... 32

Figure 32: Coupon Table results, three entries ............................................................................................................ 36

Figure 33: Market Table results, two entries ............................................................................................................... 37

Figure 34: Erlang distribution, JFK arrival delays for 2009, Q1 ................................................................................. 38

Figure 35: Poisson distribution, JFK arrival delays for 2009, Q1 ............................................................................... 38

Figure 36: Schedule Padding at N90, 2009 Q1 ............................................................................................................ 39

Figure 37: Passengers Arriving at N90 metroplex, Q1 2009 ....................................................................................... 40

Figure 38: Potential Metroplex Markets Comparison.................................................................................................. 41

Figure 39: Air Taxi and GA results 25% metroplex-bound flights ............................................................................. 53

Figure 40: Air Taxi and GA results 50% metroplex-bound flights ............................................................................. 53

Figure 41: Air Taxi and GA results 75% metroplex-bound flights ............................................................................. 54

Figure 42: Air Taxi and GA results 100% metroplex-bound flights ........................................................................... 54

Figure 43: Flight arrivals for ACES runs highlighted in Figure 42 ............................................................................. 55

Figure 44: Arrival delay probability for Air Taxi case ................................................................................................ 56

Figure 45: Arrival delay probability for GA case ........................................................................................................ 57

Figure 46: Arrival delay probability for “Both” case .................................................................................................. 57

Figure 47: ACES Smart case sample results................................................................................................................ 58

Figure 48: Arrivals and Delay comparison – ACES run 4 and 6 from Figure 47 ........................................................ 59
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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Figure 49: Baseline Air Taxi and GA arrivals at KJFK, KEWR and KLGA airports ................................................. 59

Figure 50: Arrival delay probability for “Smart” case ................................................................................................. 60

Figure 51: ACES Commercial Smart case results ....................................................................................................... 61

Figure 52: ACES Commercial only distinct SCT case – 30% flight metroplex bound ............................................... 65

Figure 53: Commercial only distinct SCT case – 50% flight metroplex bound .......................................................... 65

Figure 54: Arrival delay probability for distinct SCT case with 30% flexible flights ................................................. 66

Figure 55: Arrival delay probability for distinct SCT case with 50% flexible flights ................................................. 66

Figure 56: ACES Commercial only distinct N90 case – 30% flight metroplex bound ................................................ 67

Figure 57: ACES Commercial only distinct N90 case – 50% flight metroplex bound ................................................ 67

Figure 58: Arrival delay probability for distinct N90 case with 30% flexible flights .................................................. 68

Figure 59: Arrival delay probability for distinct N90 case with 50% flexible flights .................................................. 68

Figure 60: Flight operation comparison – N90_max and N90_Min runs .................................................................... 70

Figure 61: Traffic demand on N90 .............................................................................................................................. 71

Figure 62: Total delay for 3 airports at N90 for nominal vs. LTV-optimized flights .................................................. 74

Figure 63: Total delays for various flexible flight percentages into N90 for November 7, 2008 ................................ 75

Figure 64: Total delay for various flexible flight percentages into N90 for November 7, 2008 .................................. 76

Figure 65: Total delay comparison for the four approaches for the November 7, 2008 data file ................................ 77

Figure 66: Savings in dollars per flight by percentage of flexible flights .................................................................... 79

Figure 67: Delay distribution for the SCT metroplex airports for the nominal case vs. LTV-optimized case with 30% 

flexible flights .............................................................................................................................................................. 80

Figure 68: Nominal vs. LTV-optimized/30% flexible flight demand on the airports in the SCT metroplex .............. 81

Figure 69: Total delay reduction in SCT with and without LTV-optimization, and at various percentages of flexible 

flights ........................................................................................................................................................................... 81

Figure 70: Traffic demand and AAR (Airport Arrival Rate) for N90 airports on November 7th in 2008 ................... 82

Figure 71: Arrival delay for the ACES simulation, best case estimate, and the LTV-optimized/30% flexible flight 

case .............................................................................................................................................................................. 84

Figure 72: Capacity coverage chart of NYNJ in 2007. Note that only configurations that are used at least 1% of the 

time in the given year is marked in the figure ............................................................................................................. 91

Figure 73: Runway dependency optimization process ................................................................................................ 92

 
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

vii

LIST OF TABLES 

Table 1: Criteria (by stakeholder) relevant for Flexible Operations Concept .............................................................. 10

Table 2: Percentage of connecting passengers at popular airports from 2006 to 2008 ................................................ 11

Table 3: Number of total and candidate flexible flights at N90 big-three airports by airlines (Nov. 7, 2008) ............ 14

Table 4: Hash table is stored with ordering ................................................................................................................. 24

Table 5: DB1B Coupon and Market Fields Comparison ............................................................................................. 35

Table 6 : Coupon Table for Itinerary 2004219 ............................................................................................................ 35

Table 7: Market Table for Itinerary 2004219 .............................................................................................................. 36

Table 8: Merged Coupon and Market Tables for Itinerary 2004219 ........................................................................... 36

Table 9: Robust Airline scheduling relevant work (15) ............................................................................................... 44

Table 10: Example itinerary of a MP-bound flight operating within Traditional airline network ............................... 45

Table 11: Ticket fare class categories .......................................................................................................................... 47

Table 12: Regression Model Data Conditioning ......................................................................................................... 49

Table 13: Regression model results ............................................................................................................................. 49

Table 14: Original Flight Data Set ............................................................................................................................... 51

Table 15: ACES Experiment Design – GA and Air Taxi ............................................................................................ 52

Table 16: Airport Arrival and Departure Rates being used for ACES experiments .................................................... 55

Table 17: 100% Arrivals metroplex-bound ................................................................................................................. 56

Table 18: ACES Experiment Design – “Smart” case .................................................................................................. 58

Table 19: Arrival Delays for ACES runs 4 and 6 from Figure 47 ............................................................................... 60

Table 20: ACES Experiment Design – “Commercial Smart” case .............................................................................. 61

Table 21: Flight arrivals at N90 airports for Commercial Smart case ......................................................................... 62

Table 22: Arrival delays at N90 airports for Commercial Smart case ......................................................................... 62

Table 23: Arrivals over capacity at KJFK and KLGA for Commercial Smart case .................................................... 62

Table 24: Flights by operators - Flight Data Sets FDS_90 and FDS_SCT .................................................................. 63

Table 25: Flights by operations - Flight Data Set FDS_N90 ....................................................................................... 63

Table 26: Flights by operations – Flight Data Set FDS_SCT ...................................................................................... 64

Table 27: ACES Experiment Design – Commercial only distinct N90 and SCT case ................................................ 64

Table 28: Arrival delay analysis –Distinct N90 case ................................................................................................... 69

Table 29: AAR landing delay comparisons – Distinct N90 case ................................................................................. 69

Table 30: Scheduling example output ......................................................................................................................... 72

Table 31: Delays for Nominal Flights at 14 meter fixes and 3 airports (Non-optimized) ........................................... 73

Table 32: Delay Statistics for 3 airports at N90 for nominal vs. LTV-optimized flights ............................................ 74

Table 33: Delay reduction for various flexible flight percentages into N90 for November 7, 2008 ........................... 75

Table 34: Delay reduction for various flexible flight percentages into N90 for November 7, 2008 ........................... 76

Table 35: Estimate of the cost of delay at N90 for the nominal traffic file ................................................................. 77

Table 36: Estimate of the cost of delay for LTV-optimized flights and 30% flexible flights at N90 for November 7, 

2008 ............................................................................................................................................................................. 78

Table 37: Savings by percentage of flexible flights ..................................................................................................... 78

Table 38: Demand and delay for SCT traffic file ........................................................................................................ 80

Table 39: Arrival delays from a simple flight allocation method for N90 and SCT .................................................... 83

Table 40: Total delay comparison between the ACES simulation, the best case scenario, and the LTVoptimized/30% flexible flight case .............................................................................................................................. 83

Table 41: Total delay comparison between ACES, a best case estimate, and the LTV-optimized/30% flexible flight 

case for SCT ................................................................................................................................................................ 84

Table 42: Public transport codes .................................................................................................................................. 85

Table 43: SCT Inter-Metroplex travel using public transport at non-rush hour and rush hour .................................... 86

Table 44: N90 Inter-Metroplex travel using public transport at non-rush hour and rush hour .................................... 86

Table 45: Public transport travel time estimates to Downtown, LA ............................................................................ 87

Table 46: Public transport travel time estimates to Times Square, NY ....................................................................... 87

Table 47: Texas Transportation Institute’s Nation Congestion Tables ........................................................................ 87

Table 48: SCT Inter Metroplex travel time estimates using a cab at non-rush hour and rush hour ............................. 88

Table 49: N90 Inter Metroplex travel time estimates using a cab at non-rush hour and rush hour ............................. 88
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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Table 50: SCT cab fares to metroplex airports and Downtown, LA ........................................................................... 89

Table 51: N90 cab fares to metroplex airports and Times Square, NY ....................................................................... 89

Table 52: BOS-N90 travel cost comparisons............................................................................................................... 90
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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LIST OF ACRONYMS 

AAR Airport Arrival Rate 

ACES Airspace Concepts Evaluation System 

AOC Airline Operational Control 

ARB Airport Runway Balancer 

ARTCC Air Route Traffic Control Center 

ASDI Aircraft Situation Display to Industry 

ASPM Aviation System Performance Metrics 

AT Air Taxi 

ATC Air Traffic Control 

ATCSCC Air Traffic Control System Command Center 

ATM Air Traffic Management 

BGL Boost C++ Graph Library 

BTS Bureau of Transportation Statistics 

CCC Capacity Coverage Chart 

CNS Communication, Navigation and Surveillance 

DOT Department of Transportation

ERAM En Route Automation Modernization 

ETA Estimated Time of Arrival 

FAA Federal Aviation Administration 

FACET Future ATM Concepts Evaluation Tool 

FAR Federal Aviation Rules 

FAST Final Approach Spacing Tool 

FFS Flexible Flight Selection 

GA General Aviation

JPDO Joint Planning and Development Office 

KTG Kinematic Trajectory Generator 

LTV Linear Time-Varying 

McTMA Multicenter Traffic Management Advisor 

NAS National Airspace System 

OEP Operational Evolution Partnership 

PAX Passengers 

PDARS Performance Data Analysis and Reporting System 

RTA Required Time of Arrival 

SCT Southern California Tracon 

TFM Traffic Flow Management 

TTI Travel Time Index
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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1. Introduction 

1.1. Context: Delays at Metroplexes 

In commercial air transportation, system inefficiencies occur when air traffic demand exceeds system 

capacity. The resulting flight delays increase direct operating costs for airlines, air traffic controllers’ 

workload, and passengers’ missed connections and dissatisfaction. Moreover, the demand for air travel is 

expected to increase significantly in the future. The Federal Aviation Administration (FAA) estimated in 

2007 that the number of passengers is projected to increase by an average of 3.0 percent every year until 

2025 (1). Much of the increase in passenger demand will be in metropolitan areas usually served by two 

or more large airports. The Joint Planning and Development Office (JPDO) defines this type of region, 

with a group of two or more nearby airports whose arrival and departure operations are highly 

interdependent, as a metroplex. The New York Metroplex (N90 TRACON), for example, consists of John 

F. Kennedy (JFK) airport, LaGuardia (LGA) airport, and Newark (EWR) airport, all within driving 

distance to each other, as well as several smaller airports. Other examples of metroplexes include Chicago 

(C90 TRACON, which includes O’Hare and Midway airports) and Southern California (SCT TRACON, 

which includes Los Angeles, Long Beach, Orange County, and Van Nuys airports). The traffic to and 

from most metroplexes has increased significantly over the years. Studies reveal that N90 is reaching its 

maximum capacity under current operational rules. Therefore, increasing the capacities of N90 airports 

may be required to satisfy the growing traffic rate.(3). Because operations at these airports are interdependent, the predicted increase in demand placed on already-constrained operations has the potential to 

push flight delays to unacceptable levels, which can lead to a greater number of cancellations. Further, the 

impact of these delays is felt beyond individual flights or airports due to the propagation downstream to 

subsequent flight schedules. Addressing delays that are particular to metroplex airspace regions can also 

propagate the benefits to other airports. One method of reducing delays in a metroplex is through better 

coordination of runway usage among the airports within a metroplex. Better coordination can balance 

traffic load among metroplex airports, potentially reducing the delays by allowing some flights to use 

runways with lower demand levels (again, with additional benefits system-wide). 

There are two trends in aviation that, if not properly managed, can exacerbate delay management 

challenges. The first trend is well known: increasing traffic to and from popular metroplexes. The 

canonical example is the New York TRACON (N90). Many new concepts and approaches have been 

proposed to resolve this increase, most of them focusing on the increase of throughput (or decrease in 

delays) at or around metroplex airports. Other concepts for increasing the passenger and aircraft 

throughput are to add more metroplex capacity by using larger aircraft and/or build more runways. Yet 

another concept focuses on increasing throughput efficiency through reduction in dependencies between 

metroplex airports by “Rational” scheduling (e.g. smart scheduling) and airspace/procedure redesign. 

Increasing flexibility through efficient use of resources to exploit dependencies is another way to increase 

throughput. One example of this is metroplex runway management. Since a metroplex consists of two or 

more adjacent airports with interdependent arrival and departure operations, the coordination of 

metroplex airport runways can increase throughput at these airports. 

The second trend is that of passengers preferring the lowest-fare flight, enabled by the ubiquitous use of 

internet price-searching. Passengers will often drive to a distant airport if the airfare is low enough to 

justify the travel. Simultaneously, airlines are motivated to utilize low-throughput airports to increase 

operational predictability, reduce costs, and therefore offer the lowest price possible. While this second 

trend might seem desirable—airlines are profitably employing underutilized resources—realizing it 

complicates metroplex traffic management. As flight densities increase, the potential for conflicts also 

increases. 
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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One way to effectively manage these trends is to recognize that they exist and develop a concept for 

incorporating them into the airspace system. In other words, if passengers are willing to travel to/from 

alternate airports in a metroplex, then they might be motivated to travel on a flight where the destination 

airport can be any airport in a metroplex. This business idea may appear to be infeasible, but with 

appropriate pricing policies and service levels, it can be an attractive option for price-sensitive passengers. 

1.2. Concept Summary and Objectives of the Study 

Whether or not the underlying business model is sustainable, the idea does spawn a unique and hitherto 

unstudied operational concept. We call this the “flexible operations concept”. The underlying principle of 

this concept is the sharing of metroplex resources, in particular, the runways. Figure 1 illustrates this with 

graphs of flights bound to the three major N90 airports on November 7th in 2008. The white horizontal 

line represents the airport arrival rate (AAR) at the airport, and the Y axis represents the number of flights 

bound to N90. Traffic is congested at EWR between 1700 and 1800, but JFK and LGA have available 

slots for landing during the same period. If it is possible to share the available runways at the metroplex, 

delays might be mitigated considerably. 

Figure 1: Example snapshot of flights and runway usage at three N90 airports 

Under the flexible operations concept, airlines (and General Aviation, GA, operators) have the option of 

filing two different types of flight plans when their destination airport is at a crowded metroplex (such as 

N90, SCT, NorCal, or C90 TRACONs). Airlines may file a flight plan listing a particular destination 

airport in the metroplex, as they do today, or they would also have the option of filing a flexible flight 

plan, which lists the departure airport and the proposed departure time, as well as a route of flight to a 

decision boundary outside the destination metroplex. The destination airport is labeled as the metroplex 

identifier (e.g. N90), with no specific airport listed. The Air Traffic Management (ATM) system 

determines the destination airport and runway as the flight reaches a decision horizon outside its 

destination metroplex. 

Figure 2 represents a comparison of the hourly maximum capacity between theoretical, observed, and 

new concepts at N90. Theoretical maximum capacity is the sum of the maximum decoupled capacities of 

all N90 airports. Observed maximum capacity is the recorded maximum hourly capacity at N90 in 2009. 

Note that the box shows the runway configurations employed when maximum capacity was observed. 

There is about a 15% difference between the theoretical maximum hourly capacity and observed 

maximum hourly capacity at N90. Clarke et al. (5) reported potential for 79% reduction in delay minutes 

through scheduling with redesigned metroplex airspace (annotated as “GT MP Report” in Figure 2). It is 

expected that this delay reduction may allow N90’s hourly capacity to be higher than observed maximum 

capacity but lower than the theoretical one. The primary motivation for the flexible operations concept is 

to reduce the gap between the theoretical and realized maximum capacity. Thus, the operational value of 

the proposed concept would likely be realized in the far-term. From a purely abstract position, this 

concept can allow the ATM to maximize metroplex airport throughput, increase its resiliency to 

EWR JFK LGA
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

3

disruptions, allow maximum flexibility for users, and degrade gracefully under adverse conditions. Thus, 

the concept can be advantageous for the airlines, the FAA, and passengers. 

Figure 2: Comparison of New York-New Jersey Metroplex (N90) hourly maximum capacity between 

theoretical, observed, and new concepts. [Theoretical and observed maximums derived from Aviation System 

Performance Metrics (ASPM) data]. 

However, there are many unknown issues, including: Is the concept feasible? Can the airspace system 

handle a mixed set of traditional and flexible flight plans? What percentage of flights must file “metroplex 

only” flight plans for the system to exhibit increased efficiency, resiliency and robustness? What type of 

ground infrastructure must exist to handle connecting passengers, baggage transfers and airframe 

dislocation? At what point does this concept break down, if at all, and what elements contribute to this 

failure? Excluding issues relating to baggage transfer and airframe dislocation, all of these questions are 

addressed in detail in the following sections. The ultimate concept objective is to significantly enhance 

throughput at the key metroplexes in the NAS in a manner that does not require extensive (or expensive) 

infrastructure investment. As a first step in answering these myriad of questions, the overall study 

objective was to develop an upper bound on the possible delay reduction achieved through use of the 

concept, cognizant that later studies must incorporate more of the practical constraints and reassess the 

benefits.

2. Flexible Operations Concept 

2.1. Operator CONOPs 

This section presents a narrative of how the overall concept would work from the airline operator’s 

perspective and also from the passenger’s perspective. Currently, a flight departing from a departure 

location is scheduled to a fixed arrival airport. Once the flexible operations concept is introduced a flight 

can be scheduled to a metroplex instead of an airport, i.e. a flight can arrive at any of the airports that 

comprise a metroplex. The new concept allows for two types of flight plans, one in which the destination 

is fixed, as done today, and another in which the destination is labeled as a metroplex (exemplified in 

Figure 3). Therefore, the concept will have both “airport-bound flights” and “metroplex-bound flights”.
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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Figure 3: On left: Flight plan with fixed destination airport, as done today; On right: Flight plan with 

metroplex destination 

In the flexible operations concept, the exact destination airport and runway assignment of the flight will 

be made at a decision horizon outside the airport. Until the aircraft reaches that decision horizon, neither 

the operator nor the ATC system knows the exact destination airport. 

2.2. Potential Passenger CONOPs 

From the passenger’s perspective, for the new concept to work, a very reliable ground transportation link 

must exist between all the airports in a metroplex as well as from airport to key points of interest in the 

local area. In addition to easy access to ground transport, the passenger must also not have to lose time 

and money by choosing a metroplex-bound flight over a traditional flight. The following vignette presents 

a potential use-case scenario of the new concept based on how a passenger purchases tickets from an 

internet travel website such as Expedia.

Internet travel websites are the main source of information on flights (and other travel related services) for 

most airline travelers in the US. Planning includes options such as flight+car, flight+hotel, 

flight+car+hotel and so on. In order for the flexible operations concept to be practical, a passenger must 

be able to conveniently reach his or her final destination from any of the constituent airports in the 

metroplex. In addition, availability of information on the time and money saved by choosing a metroplexbound flight will potentially lead to a larger number of metroplex travelers choosing metroplex-bound 

flights over traditional flights. Figure 4 presents the current Expedia (6) interface in which the destination 

airport is fixed (JFK in this case), which is the common practice today. 
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Figure 4: Snapshot of current Expedia® interface (© Expedia, Inc.) with destination listed as an airport 

Some travel websites allow travelers to search for tickets to a particular city instead of an airport, whereby 

they allow them to search for more convenient prices and times. The websites also provide travelers with 

information on car rental facilities at the airport allowing for advance reservations through the website as 

well. Under a flexible operations concept, the travel websites could adopt a similar approach but with a 

more extensive listing of not only flight departure/arrival time and ticket fare but also ground 

transportation links. These links, as mentioned above, should include airport to airport as well as airport 

to popular locations within the destination city. Figure 5 shows how Expedia might appear if the flexible 

operations concept is in place; note that travelers now have the option to choose metroplex-bound flights. 

Figure 5: Altered interface with metroplex-bound option included 

If “Metroplex” option is chosen, the traveler would be presented with flights that the airlines plan to 

operate as “metroplex bound”. In considering their choices, travelers would have information to compare 

available ground transport links in terms of mode, timings and price. Above all, travelers must also be 

able to compare prices of metroplex-bound flights to traditional, fixed airport flights so that they can be 

aware of the economic advantages or disadvantages associated with such flights. Such information must 

be supplied to a traveler in advance in order to persuade him or her to buy a metroplex-bound flight. 
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3. Potential Benefits and Barriers of Flexible Operations Concept 

3.1. Potential Benefits 

The concept of flexible flights into a metroplex has two main advantages. First, it allows the air traffic 

control system to maximize resource utilization (runways, in this case) in an otherwise tightly constrained 

system. Second, it allows users to experience less delay when accessing crowded metroplex airports. We 

expect that the early adopters of this type of flight plan will be on-demand and general aviation users, and 

that the hub-and-spoke commercial air carriers will follow only if a clear benefit is very likely. In this 

sub-section, we review the major advantages from the perspective of all stakeholders: the FAA, the air 

taxi community, the general aviation community, the traditional commercial airline operators, and 

passengers. 

FAA perspective: From the point of view of an air navigation service provider (ANSP), such as the FAA, 

metroplex-bound flights allow maximum flexibility in the planning and use of runways at a metroplex, 

especially considering the dynamic conditions that affect airport configuration and capacity (e.g., 

changing winds, changing visibility, stochastic demand realization). The realized metroplex capacity can 

thereby be increased without adding any additional runways. 

There is a second, albeit indirect, benefit of the flexible operations concept for the ANSP. If metroplexbound flight plans were to be supported in some future version of the NAS, then the flight planning 

system (e.g., En Route Automation Modernization (ERAM) and its supporting subsystems) must be 

programmed to determine the best available allocation of a flight when it reaches the decision horizon. In 

doing so, the system will consider a number of variables and assign the flight to the best available 

resource. The indirect benefit follows from the introduction of this capability into the planning 

environment. In the case of an emergency—perhaps an airport is completely closed due to weather, an 

accident or some other disruption—the airborne flights affected by the closed airport (or other 

emergency) will already be in the planning system; the system will be prepared to determine what course 

of action to take with these flights, alleviating this part of the workload from controllers. The airborne 

flights at the time of the disruption would be marked by controllers as “metroplex-bound” flights. The 

affected airport would be eliminated from consideration, and the planning system would then begin to 

assign these flights to the most efficient available runway, even considering other traffic already bound to 

the open airports. 

Air-taxi (on-demand operators) perspective: There are some advantages that are bestowed upon the airtaxi community with this concept. Currently, air-taxi operators fly between one and six people between 

local airports and sometimes one of the airports lie within a metroplex. A typical air taxi flight might 

consist of three lawyers who have to travel to the state capital for a brief. Travel using air taxi allows 

them to complete the entire trip within one day, a considerable savings of time if they live far away from 

the capital. In such a scenario (and many similar ones in the air taxi community), the exact airport that the 

passengers arrive at the state capital is of less concern to them than that they are on time. If the state 

capital happens to be in a metroplex, then metroplex-bound flight plans would be an ideal option for the 

air taxi operator. 

In discussions with air taxi operators, we discovered that the concept of dynamically changing the 

destination airport can be done even with today’s system, although the mechanism is inefficient. While 

airborne, an operator can re-file a flight plan and list a destination different than the original flight plan. 

This technique is useful if excessive traffic or weather causes the originally filed destination airport to be 

less attractive than a nearby airport. One air taxi operation in Florida would occasionally use this 
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technique on their flights to the Miami area. 

General aviation perspective: The general aviation community frequently flies into smaller airports and 

avoids the major congested airports. Nevertheless, when flying into a popular destination such as the New 

York metroplex, a general aviation operator would often rather be routed to the most efficient destination, 

as opposed to a filed, fixed destination which might be congested at the proposed landing time. In this 

model, it is assumed that the general aviation operator is flying from his/her home base to another 

location. If instead he or she is returning to the aircraft’s home base (where it is hangared and serviced), 

then an airport-bound flight plan would have to be filed. 

Commercial air carrier perspective: Advantages to the traditional commercial air carriers are more 

difficult to discern; while they would benefit from overall reduced delays at metroplex airports, their 

particular constraints could complicate use of flexible operations. Commercial carriers have fixed gates, 

ground crews at predetermined locations, ticket agents and baggage handlers only at certain airports. Thus 

their operations are set up to exploit the current airport-bound flight plans. For commercial carriers to 

effectively use a metroplex-bound flight plan, the business model would have to be rethought. If a 

commercial carrier has operations (i.e. gates, gate agents, baggage handlers, ticket counters) at all three 

major airports in N90 (JFK, EWR, and LGA), it can, in theory, file a metroplex-bound flight plan, and 

allow the system to route the flights to the most efficient destination. However, the system’s definition of 

“most efficient” may not take into account whether there is gate availability for the commercial carrier at 

the assigned destination. For example, if too many metroplex-bound commercial flights for this 

(hypothetical) carrier are routed to the same airport, the carrier may use up its available gates quickly and 

be faced with other problems.

The flexible operations concept does, however, generate ideas for new business models. Imagine a future 

in which the gates, gate agents, and ticket sales were all decoupled from the airlines. That is, airline XYZ 

no longer had its own ground crew; instead, it relied upon a common set of ground crew and resources 

available at the airport. This type of pooling of ground resources has, all by itself, advantages and 

disadvantages. The advantages are economies of scale: by pooling ground resources, duplication is 

avoided and the efficiencies that are gained with uniform procedures and equipment can be exploited. The 

disadvantages are from a marketing perspective: airlines would no longer be able to differentiate 

themselves by offering superior ground service. 

These considerations aside, if ground services were a pooled resource that could be shared by multiple 

airlines, then problems of ground and even gate availability are eliminated from consideration. All that is 

left is repositioning of flights and getting passengers to/from their final destination. The latter problem—

getting passengers to/from their ultimate destination—is not even a problem the airlines solve today. 

Rather, the passengers are dependent upon local ground transportation. The former problem—

repositioning airframes and crews—can be alleviated by offering “metroplex-bound” departures, wherein 

an airline promises passengers a seat on an aircraft, but tells the passenger from what airport he/she will 

be departing a few hours before the flight—enough time in advance for the individual to plan the ground 

transportation to the airport. The communication of the specific departure airport can be accomplished 

with contemporary media—text messages on a cell phone, an email, posting on a web site, or a phone 

call. Thus a possible future business model for a commercial carrier to exploit this idea would consist of 

ground crews which are a pooled resource, combined with flights that are metroplex-bound both on the 

arriving and departing end. Although this business model is a possibility, further analysis of this 

possibility is beyond the scope of this project. 
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3.2. Potential Barriers 

The main barrier of the flexible operations concept is the uncertainty that it adds to user operations. If a 

user does not know exactly where the aircraft will land, planning may be problematic. Sufficient fuel 

must be loaded on to the aircraft to reach the furthest airport in the metroplex plus the required reserves, 

which itself depends upon whether the destination airport is operating under visual or instrument 

conditions at the projected time of arrival. For example, some of the airports in a metroplex might be 

forecasted as having visual conditions at the expected arrival time, but others might be forecasted as 

having instrument conditions. This consideration alone would require rulemaking to clarify what the fuel 

requirements are for metroplex-bound flights. For example, the rule might be that the carrier must use the 

airport for which the forecast is the worst when planning fuel loading, and must plan a trip length that 

would reach the farthest airport in the metroplex from the originating airport. 

The uncertainty in planning affects all user groups. The service provider (i.e. FAA) will no longer have an 

accurate forecast of demand at each airport; instead, it would be an accurate forecast for each metroplex. 

In addition, the service provider is faced with the challenge of developing and building an algorithm that 

can effectively exploit the apparent efficiencies of metroplex-bound flights (i.e. build a scheduling 

algorithm that efficiently assigns metroplex-bound flights to destinations). For commercial airlines, the 

challenge starts with the problem of repositioning flights for departures. The airline may have to 

reposition crew, passengers, baggage, and even the airframes themselves. There have been many studies 

to date on recovering airline operations after a disruption. One of the techniques is to have frequent flights 

to popular destinations, so there are more choices for passengers; this method of operation allows the 

airlines to rapidly recover from a disruption. As will be discussed later, this and other considerations led 

us to conclude that short-haul “shuttle” flights with few connecting passengers are likely to be the most 

attractive choices for airlines for metroplex-bound tickets—although even those flights produce 

operational challenges. 

All these challenges stem from the basic problem of additional uncertainty that is added to the operations. 

For passengers, the problem is (and has always been) traveling to the final destination. In the case of nonconnecting passengers, the airport itself is not the final destination. He/she must rely on personal car, taxi, 

or public transport. In addition, if the passenger is connecting to another outbound flight that happens to 

be at a different airport, then additional time must be built into the passenger’s schedule to account for the 

inter-airport transportation that is required. 

4. Technical Approach 

4.1. Model Overview 

An integrated simulation model was developed to validate the capabilities of a new concept in the U.S. 

National Airspace System (NAS). The model, in general, was developed to solve two classes of problems: 

a NAS-wide flight route optimization problem and a time-based metering/scheduling problem for enroute air traffic control associated with a group of adjacent sectors. As such, the model can analyze the 

flexible operations concept in metroplex regions. The model can estimate the impact of the new concept 

on system performance, not only at a single metroplex, but also at multiple metroplexes. The systemperformance measure obtained from the integrated simulation model is total delay. 

The integrated simulation model is comprised of five principal components (Figure 6). The model uses a 

variety of data to generate statistical distributions for the selection of flexible flights as a concept design 

variable and to describe the airport and airspace route network. The five principal components in the 
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model are described next. 

Figure 6: Overview of the integrated simulation model 

1) Flexible Flight Selection (FFS) model: The FFS generates a flight data set (daily schedule) that is 

modified to include those flights designated as flexible flights. In detail, the model is employed to 

select flexible flights among NAS-wide flights based on the number of connecting passengers. 

Historical data of the number of connecting passengers are used to compute the probability of 

desirable flexible flights. 

2) Linear Time-Varying (LTV) Optimizer model: LTV Optimizer model, originally developed by 

NASA Ames researchers, takes the flight data set as input and produces optimal routes for all 

flights to destinations, including estimated time of arrivals (ETAs) to key points. LTV solves a 

large integer programming optimization problem for this purpose 

3) Kinematic Trajectory Generator (KTG): KTG is a product developed by IAI. Its main purpose is 

to provide high-resolution trajectory computations from the departure airport runway to the 

arrival airport runway. 

4) Multicenter Traffic Management Advisor (McTMA) model: The McTMA extends TMA, a timebased metering tool for en-route air traffic control and management, for both in- and out-bound 

time-based metering within multiple centers. In addition, the model is used to schedule flights to 

runways for the optimized routes computed by LTV Optimizer model. 

5) Airspace Concepts Evaluation System (ACES): ACES is a physics-based, fast-time simulation of 

the nationwide air traffic system, including air traffic management (ATM), flight, and airline 

operational control (AOC) functions. ACES is employed to test McTMA and optimal scheduling 

algorithms and compute NAS-wide impacts of the flexible metroplex operations concept. 

4.2. Flexible Flights Selection (FFS) 

4.2.1. Overview of FFS Model 

The first step in the modeling of flexible flights concept is to select flexible flights as a concept design 

LTV McTMA

Trajectory 

model

NAS wide 

flight plans 

for 1-Day

Routes

ETAs

Define meter 

points

Define 

Streams

Select Streams 

to Schedule

Evaluation of 

Metroplex 

Performance w/ 

Flexible Flights

t=t2 t=t1

Runway 

balancer/ 

scheduler runs 

(using available 

MP runways)

FFS

Flexible 

flights

(a Concept 

Design 

Variable)

ACES

KTG

FFS = Flexible Flight Selection Model

LTV = Linear, Time-Invariant Optimizer

McTMA= Multi-Center Traffic Management Advisor

ACES = Airspace Concept Evaluation System
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variable. The FFS model computes the probability of desirable flexible flights based on criteria described 

below. Once the FFS model determines flexible flights from a NAS-wide flight plan available for a day, it 

provides an output file, including both flexible flights and normal flights, to the LTV Optimizer model. 

4.2.2. Criteria for Flexible Operations 

In order to determine flexible flights, interests of airline operators and passengers are considered. Airline 

operators are interested in maximizing profit and minimizing crew and fleet disruptions. However, 

passengers usually consider price, connection quality or need for connection and predictability of arrival 

time to select a flight and operator. In the case of flexible operations, interests of these stakeholders 

remain the same. For example, operators decide what flights and routes are used for flexible operations 

based on decision criteria such as maximizing profit and minimizing crew and fleet disruptions. 

Passengers can decide whether they use a flexible flight or a normal flight based on price and connection 

qualities. The list of flexible operations criteria for both operators and passengers is described in Table 1. 

Table 1: Criteria (by stakeholder) relevant for Flexible Operations Concept 

Stakeholder Criteria for Flexible Operations 

Commercial 

Operator

Scheduled 

Operator

Hub-to-Spoke 

- Minimize fleet disruption 

(Depends on heterogeneity of fleet mix) 

- Minimize crew disruption 

- Maximize profit 

In-between Hub-toSpoke and Spoke-toSpoke 

- Minimize crew disruption 

- Maximize profit 

New Business Model 

(Only point-to-point 

service) 

- Minimize crew & fleet disruption 

- Maximize profit 

Non-Scheduled 

Operator

Business/Factional/Ch

arter

- Minimize operating costs 

- Maximize profit 

Passenger 

- Minimize price 

- Connection quality or need for connection 

- Improve predictability of arrival time 

A hypothesized governing equation merging criteria for flexible operations consists of several 

components, including connecting passenger models, crew/fleet disruption models, etc (Table 1). Aircraft 

types (for those service providers with heterogeneous fleets) can also impact crew and fleet disruption 

levels. If airlines were to adopt the flexible operations concept, they have to reschedule their flight plans 

to optimize profits and to reduce disruption level using their own tools for crew coordination and 

operations management. Due to the lack of sufficient sources to analyze disruption level, it is difficult to 

develop probabilistic models for crew and fleet disruption in the FFS. Therefore, in the study described in 

this report, the only design variable in the flexible flight concept model is the criterion of connecting 

passengers – the minimum number of passengers above which a flight will be deemed unqualified for 

flexible operations.

4.2.3. Data Sources 

Two databases maintained by the U.S. Bureau of Transportation Statistics (BTS) are used: 

1) The T-100 Domestic segment (All US Carriers) database contains data on a monthly basis, including 

aircraft type, service class, seats and aircraft hours: ramp-to-ramp and airborne. Key values which are 

used for the analysis are flight date, departure/arrival airports, number of passengers on board, number of 
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flights, and airline information. 

2) Airline Origin and Destination Survey (DB1B) database is a 10% sample of airline tickets from 

reporting carriers. Data includes origin, destination and other itinerary details of passengers transported. 

Also, data is provided on a quarterly basis. 

4.2.4. How Many Passengers Connect? 

The ‘probability of connecting passengers’ is the only variable in the FFS model and it value was derived 

from historical data. DB1B data were used to estimate the proportion connecting passengers at an airport. 

Since DB1B data only report a 10% sample of airlines tickets, the exact number of connecting passengers 

at an airport could not be determined. The percentage of connecting passengers is defined as the ratio of 

the total number of connecting passengers to the total number of arriving passengers at an airport. As 

expected, airline hub airports will have many connecting passengers, as seen for example at ATL (Table 

2) which is a major connecting hub of Delta and AirTran airlines. SFO and JFK have elevated values 

since SFO is a hub of United and American airlines and JFK is a hub of JetBlue, Delta, and American 

airlines (based on 2006-2008 data). Among the N90 metroplex airports, JFK has the highest percentage of 

connecting passengers followed by EWR. LGA has the least. From this information one may expect for 

LGA to provide most of the candidate flexible flights for N90. 

Table 2: Percentage of connecting passengers at popular airports from 2006 to 2008 

Year EWR JFK LGA

% connecting % connecting % connecting 

2006 12.42 14.49 6.97 

2007 11.96 18.23 7.52 

2008 13.83 17.18 7.88 

Year ATL SFO SJC

% connecting % connecting % connecting 

2006 61.55 21.85 6.82 

2007 62.27 20.93 6.58 

2008 63.85 17.85 5.92 

4.2.5. Development of FFS Model based on the Number of Connecting Passengers 

The FFS model uses both the T-100 Domestic segment (All US Carriers) database and the DB1B 

database to generate a probability of connecting passengers. The FFS algorithm consists of two processes: 

the first process constructs connecting passenger dataset and the second process computes probability of 

connecting passengers. The first process in the FFS is summarized on left of Figure 7, and involves 

collection of data on the number of passengers and flights between each pair of airports for an airline 

from T-100 Domestic segment database and data to compute the ratio of connecting to non-connecting 

passengers for each route connected to metroplex airports using DB1B. The data are processed for 

analysis on a quarter-year time scale. 

The number of connecting passengers per flight per airline per route is obtained using the number of 

connecting passengers and the ratio of connecting to non-connecting passengers between each pair of 

airports for an airline. In the last step, we assume all passengers on a route for an airline are equally 

distributed to all flights on that route. The flowchart on right of Figure 7 shows the second process that 

computes the probability of a flight having connecting passengers less than or equal to a criterion using 

the result from the first process. This evaluation step is iterated 17 times from 1993 to 2009 (entire BTS 
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data availability time span). Subsequently, the probability of a flight having connecting passengers less 

than or equal to the criterion number, 5, is obtained for every flight in the given 1-day flight plans. 

Figure 7: Two processes for FFS model. ‘MP’ = Metroplex 

In summary, input variables used in the FFS include departure and arrival airports, airline type, and 

departure time of a flight in a 1-day flight plan. The output of the FFS is, for all commercial airline flights 

in the flight data set, the probability of each flight having connecting passengers less than or equal to the 

criterion number. An example working of FFS for Q3, 2008 with JFK as the destination airport is 

described next and illustrated in the histogram displayed in Figure 8. In Figure 8, the x-axis is origin 

airport index number (list of 348 airports in the flight data set) and the y-axis is the airline index (list of 

238 airlines represented in the flight data set). The z-axis is the resulting probability of their being fewer 

than 5 connecting passengers on a flight for a particular route and airline in Q3, 2008 into JFK. Consider 

a United Airlines flight from DEN to JFK (coordinates on the plot are 77, 205). FFS gives an output of 

0.7975 (highlighted in red on the plot) which means that when randomly choosing a UA flight from DEN 

to JFK in Q3 2008, the probability that such a flight has 5 or fewer connecting passengers is 79.75%. The 

figure also highlights two other cases, for different origin airports and airline. 

Figure 8: Distribution of FFS’s outputs from histogram approach for Q3 2008 flights arriving at JFK 
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4.2.6. Simple Example of Selecting Flexible Flights using FFS model 

Figure 9 illustrates an example use of the FFS output. Consider five flights on a given 1-day flight plan 

with each having its own departure and arrival airports, departure time, and operator. Given these 

conditions, FFS produces the probability of a flight having fewer than or equal to 5 connecting passengers 

for each flight. The probability is defined as Pr(C-PAX<=5|Departure, Arrival, Airline, Time) and each 

flight will have a probability attached to it. The percentage of flights selected as a flexible flights is a 

concept design variable whose value dictates the modified flight dataset provided by FFS to the integrated 

model. In this example, this concept design variable has a value of 0.6 (3 out of 5 flights). The next step 

includes selection of three flights with the highest probability of connecting passengers less than the 

criterion of 5. The larger the probability the more likely it is for a flight to have connecting passengers 

less than or equal to 5. FFS provides two groups: in this case, Flexible Flight Group with 3 flights and 

Normal Flight Group with 2 flights. 

Figure 9: Notional selection of flexible flights using FFS model 

4.2.7. Application of FFS Model to N90 and SCT Metroplex 

The application of the FFS model begins with an assessment of the traffic on the day we use to analyze 

our concept. On that day (November 7, 2008), 1,706 flights including commercial, GA (General 

Aviation), and AT (Air Taxi) arrived at N90’s three big airports (JFK, LGA, and EWR). Figure 10 shows 

the distribution of flights arriving at these three airports on that day, indicating an even distribution of 

flights across all three airports.

Figure 10: Distribution of flights arriving at N90 big-three airports on November 7th in 2008 

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

JFK LGA EWR

% flight arriving at one of N90 airports 7/11/2008
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We choose 30% (other levels of flexibility are addressed in the results section) as the design variable 

setting for the percentage of flexible flights we use to test the concept. In the case of N90, 511 

commercial flights (30% of all flights) were selected from all flights arriving at one of the N90 big-three 

airports as flexible flights. The selection was based on the probability of a flight having connecting 

passengers less than or equal to 5. Figure 11 represents the distribution of FFS-selected candidate flights. 

As expected, results indicate that FFS takes the largest percentage of flexible flights from LGA followed 

by EWR and JFK, since a larger number of passengers use JFK as their connecting airport, with LGA 

having the lowest number of connecting passengers. 

Figure 11: Distribution of FFS-selected candidate flights arriving at N90 big-three airports 

In order to identify trends for choosing flexible flights by airlines at N90’s “big three” airports, it was 

important to see the distribution of total flights and candidate flexible flights from the FFS model by 

airlines. Table 3 shows the number of total and candidate flexible flights arriving at N90’s three big 

airports by airlines, compiled by using FFS on historical data (1993-2009). The largest number (13.5%) 

of total flights at N90 airports are operated by Continental airlines. ExpressJet airline tops the list when it 

comes to flexible flights; thus, ExpressJet airline does not have many connecting flights at N90 airports. 

The largest number of flexible flights at EWR is from ExpressJet Airlines. At LGA, American, America 

West, and Delta airlines have large portion of flexible flights. 

Table 3: Number of total and candidate flexible flights at N90 big-three airports by airlines (Nov. 7, 2008) 

The distribution of flights that are not selected as flexible flights is presented in Figure 12 in order to 

show which airlines operate flights that are not good candidates. At EWR, Continental and ExpressJet 

airlines flights are likely to have more connecting passengers than other airlines, since EWR is their Hub 

airport. The same trend can be observed at JFK, but for different airlines: American, Delta Airlines and 

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

JFK LGA EWR

Airlines JFK LGA EWR % flights JFK LGA EWR % flights

American Airlines 51 57 14 7.2% 2 55 10 13.1%

America West Airlines 6 42 12 3.5% 6 42 12 11.7%

Delta Air Lines 70 57 12 8.1% 6 50 9 12.7%

American Eagle Airlines 32 44 0 4.5% 9 30 0 7.6%

Northwest Airlines 6 21 9 2.1% 5 21 6 6.3%

United Airlines 13 20 15 2.8% 6 15 11 6.3%

AirTran Airways 0 17 5 1.3% 0 17 5 4.3%

JetBlue Airways 145 8 12 9.7% 1 8 12 4.1%

ExpressJet Airlines 0 0 141 8.3% 0 0 87 17.0%

Continental Airlines 0 17 213 13.5% 0 17 6 4.5%

60.9% 87.7%

Total Flights Candidate Flexible Flights
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JetBlue Airways. 

Figure 12: Distribution among flights NOT candidates for flexible operations 

At the SCT metroplex, 294 commercial flights (30% of all flights) are selected from all flights (980 

flights) arriving at one of the SCT airports as flexible flights based on the same connecting passenger 

criterion used in the N90 case. Results indicate that FFS chooses the largest number of flights from LAX, 

followed by SNA and BUR as shown on the left of Figure 13. Figure 13 also shows that flights at LAX 

that are provided by United, Delta, and American airlines have more connecting passengers than other 

airlines because LAX is the hub city of these airlines. The story is similar at LGB. Since LGB is the hub 

city of JetBlue Airways, the number of connecting passengers in JetBlue at LGB is high as shown in the 

figure.

Figure 13: Distributions of FFS-selected candidate flights (Left) and flights NOT candidates for flexible 

operations (Right) for SCT metroplex airports 

4.2.8. Factors addressed by the FFS model 

The FFS model considers four important factors in generating probabilistic results: particular airline, time 

period, origin airport, and destination airport. Therefore, the model can capture these key characteristics 

of each flight in a schedule. For example, if a destination airport of a particular flight is not a hub of the 

operating airline, then the FFS model will output a low probability of connecting passengers. Also, if a 

flight is occurring on a point-to-point route, then the model will output a low probability of connecting 

passengers for that flight. However the FFS model currently cannot consider time of day when analyzing 

0

50

100

150

200

250

JFK LGA EWR

AmericanAirlines

America West Airlines

Delta Air Lines

American Eagle Airlines

Northwest Airlines

UnitedAirlines

AirTranAirways

JetBlue Airways

ExpressJet Airlines

Continental Airlines

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

LAX ONT LGB SNA BUR

0

10

20

30

40

50

60

70

80

90

100

LAX ONT LGB SNA BUR

AmericanAirlines

America West Airlines

Southwest Airlines

Delta Air Lines

AmericanEagle Airlines

Northwest Airlines

United Airlines

SkyWest

JetBlue Airways

Frontier Airlines

Continental Airlines
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the historical flight data (for example, a flight that arrives at midnight, despite all other features, is 

unlikely to have many connecting passengers). Finally, the FFS does not address airline fleet management 

and crew management aspects. 

4.3. Linear Time-Varying (LTV) Optimizer 

The Linear Time Varying (LTV) Optimizer was developed by NASA Ames researchers (7). It is designed 

to compute NAS wide optimized flight routes and the ETAs. 

4.3.1. LTV Overview 

The number of aircraft at different times in each center is represented by a state variable. The number of 

landings in a center and transitions from the center to the neighboring centers in an interval of time  ̈T are 

assumed to be proportional to the number of aircraft in the center at the beginning of the interval. Using 

the principle of conservation of Àow in a center, the number of aircraft in center i at the next instant of 

time k + 1 can be related to the number of aircraft in i at k via the difference in the number of aircraft that 

came into the center and the number of aircraft that left the center as follows: 

The number of arrivals in center i during the k

th time interval, denoted by ୧ሺሻ ൌ Ⱦ୧୧ሺሻ୧ሺሻ, is a 

fraction of ୧ሺሻ. In special cases, Ⱦ୧୧ሺሻ ൌ Ͳ implies that a transition must involve crossing an airspace 

boundary, and Ⱦ୧୧ሺሻ ൌ ͳ implies physical transition from one time interval to another time interval. The 

departure within center i is denoted by ୧ሺሻ, which is independent of ୧ሺሻ. For simplicity of illustration, 

“during the kth time interval  ̈T” will also be understood as “at time k” here. 

The inputs to the LTV include departures ୧ሺሻ within center i at time k and the fractions Ⱦ୧୨ሺሻ. The 

direct output from the LTV is the aircraft count in the centers at each time step. It is also straightforward 

to generate other outputs, such as intercenter traffic Àows, number of arrivals, etc., based on the 

information contained in variables ୧ሺሻ, ୧ሺሻǡ Ⱦ୧୨ሺሻ.

To implement the LTV, the fractions Ⱦ୧୨ሺሻ are obtained as the aggregated fractions of aircraft going from 

center i to j during the same k

th time interval in each day, using historic air traffic data. The departures 

୧ሺሻ are computed from ¿led Àight plans (deterministic). This is different from the original model in [7], 

which includes both deterministic and stochastic components. The LTV model is implemented in a 

deterministic manner because a deterministic optimization method can be used for traffic flow 

management (TFM). All centers of the U.S. airspace, oceanic centers, and part of the Canadian centers 

are included in the LTV. 

LTV is designed to optimize flight routes and ETAs for traffic flow management. The selection of Ⱦ୧୨ሺሻ

and ୧ሺሻ is equivalent to a traf¿c Àow management strategy based on choosing optimal routes and 

aircraft ETAs to minimize a cost function. 

Our optimization problem is formulated as follows: 
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The constraints and the cost function are described in the following subsections. 

1) Initial Condition 

The dynamics equation is presented at the very beginning of LTV Overview section. 

2) Dynamics 

The time-varying center capacity constraints restrict the number of aircraft in a center to be below the 

maximum number of aircraft allowed. 

3) Airspace Capacity 

Airport capacity includes departure constraint and arrival constraint. The departure constraint restricts 

the number of departures below the departure capacity at each time step. The arrival constraint 

restricts the number of arrivals below the arrival capacity. 

4) Minimum Dwell Time 

The amount of time an aircraft spends in a center depends on the design speed of the aircraft and the 

Àight route in the center. The minimum dwell time is assumed to be identical for every Àight Àying 

through a center despite the fact that Àights use different routes that need different travel times, that 

there are Àights taking off and landing in the same center, and that Àights taking off and landing have 

different dwell times than en route Àights, etc. 

The formulation of minimum dwell time constraints for the general case introduces one constraint for 

each route, whereas the special case has one constraint for each center; therefore, it is expected that 

the general case will take more computational time for the optimization. 

5) Objective Function 

Initial condition

Dynamics

Airspace capacity

Airport capacity

Min dwell time
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The cost function is defined as follows: yሺሻ ൌ σ σ ୧ሺሻ ୘

୩ୀ଴

୒

୧ୀଵ

which is equivalent to minimizing the total Àight time. 

4.3.2. Virtualization for N90 and SCT Metroplexes 

The NASA-developed FACET software is used to visualize flows. Figure 14 shows the N90-bound and 

SCT-bound traffic flow pattern and the zoom-in pictures. The Figure is generated by a playback of 

historical ASDI data, from a good weather day, 08/24/2005. 

Figure 14: Traffic flow pattern of N90-bound 

For N90, only JFK, EWR and LGA are considered in these visualizations. Figure 14 is plotted with 

flights whose destination is JFK, EWR or LGA. A zoom-in is displayed in Figure 15 and Figure 16, 

showing some of the common waypoints and arrival fixes which facilitate us to find out and define the 

metroplex boundaries. 
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Figure 15: Traffic flow pattern of N90-bound zoom in 1 

Figure 16: Traffic flow pattern of N90-bound zoom in 2 

LAX, BUR, ONT, LGB and SNA are considered in the SCT metroplex shown in Figure 17. The zoomed 

in plots Figure 18 and Figure 19 provide views of arrival fixes and common waypoints around SCT. 
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Figure 17: Traffic flow pattern of SCT-bound 

Figure 18: Traffic flow pattern of SCT-bound zoom in 1 
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Figure 19: Traffic flow pattern of SCT-bound zoom in 2 

Finally, both traffic flow patterns of N90 and SCT are shown in Figure 20. 

Figure 20: Traffic flow pattern of N90 and SCT 
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4.3.3. Data inputs to LTV 

A network data structure is required in order to apply the efficient and effective LTV metroplex routing 

algorithm. The waypoint-based network is exploited as the data structure to generate the optimal route for 

each flexible flight under sector congestion constraints. This initial network is built from waypoints 

provided by Future ATM Concepts Evaluation Tool (FACET) and is named as Jetway Network. Next, an 

extended ‘Jetway Plus Network’ is formulated by inserting additional waypoints; the ‘Jetway Plus 

Network’ is used as the final data structure for running LTV. The development of these networks from 

FACET is described next. 

4.3.3.1. Obtain the waypoints and jet routes information from FACET

To accurately capture the real flight paths that air traffic follow, a jet route-based waypoint network is 

built. First, jet route and waypoint information are obtained from FACET. Using the NavigationInterface 

in FACET, the waypoint identifiers (index) and locations (longitude and latitude) are obtained for all 

waypoints. Also using the NavigationInterface, the sequence of waypoints that defines a jet route is 

obtained for all jet routes. To convert a jet route description in terms of waypoint locations to waypoint 

indices, the corresponding index is identified for each waypoint on a jet route using the waypoint 

information from FACET. 

To form the network, we record the connection (edge) between two consecutive waypoints (vertices) for 

each jet route. Intersection points on the jet route system are automatically considered as waypoints when 

recording the connection along a jet route. Figure 21 illustrates how edges are obtained from a jet route, 

along with intersection points. Additional edges were created to connect airports to the jet route system. 

Collectively, the edges and vertices define the graph. The edges are not directed because a jet route can 

support traffic in both directions (in different altitudes). 

Figure 21: Obtaining graph edges from jet route, including intersection points 

4.3.3.2. Establish the Jetway Network

In order to take advantage of the Boost C++ Graph Library (BGL) [3], we transform the obtained 

waypoints and the jet routes data into the BGL format. C++ BGL is an efficient tool for network analysis 

and optimization. The major advantage of the BGL is that it has a generic interface that allows access to 

a graph's structure, but hides the details of the implementation. This is an “open" interface in the sense 

that any graph library that implements this interface will be interoperable with the BGL generic 
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algorithms and with other algorithms that also use this interface. The BGL provides some general purpose 

graph classes that conform to this interface. 

According to the requirement of C++ BGL, we have each waypoint provided by FACET in a C++ struct 

and put all these waypoint structs in an indexed C++ map. Each struct contains the name, latitude, 

longitude information of the waypoint. The waypoints serve as the nodes in a Jetway Network graph. 

Each direct route between two nodes is considered as two directional edges in a Jetway Network graph 

and each of the two is recorded in a C++ struct which contains the starting point and the ending point. All 

the edges are also stored in an indexed C++ map. At the beginning of the network construction, the great 

circle distance of each edge is calculated and recorded as the non-negative weight in each edge struct. A 

Jetway Network graph is then built based on the waypoints and direct routes as nodes and edges 

respectively. Finally, a shortest-path algorithm (Dijkstra’s Algorithm) provides the results of all the 

shortest paths from the origin airport to every other airport except those disconnected from the origin. 

4.3.3.3. Hash Table

In order to insert new waypoints and dynamically maintain the network, the waypoints need to be indexed 

by a hash table. The similar hash table is built for direct route data. For simplicity, we only show how to 

build the hash table for waypoints in this section. The hash key in this paper is the rounded positive 

latitude-longitude pair of each waypoint; in other words, each waypoint's latitude and longitude are 

changed to their absolute values and then rounded into integers. Then the positive integers are stored in a 

hash key pair <lat, lon>. All of the existing waypoints in Jetway Network are processed to acquire their 

hash key pair. Initially the hash table H is set to be empty (ij). The hash key pair <lat, lon> is calculated 

according to the latitude and longitude of a waypoint ୧ and a search is started in the current hash table 

H based on <lat, lon>. If this hash key pair is not in H, a <lat, lon> is created in H and this waypoint ୧

is inserted under the newly created hash key pair; if it is already in the hash table, compare ୧ to all the 

waypoints under this existed hash key pair, insert ୧ if no same waypoint is found. This process is 

repeated until all the waypoints obtained from FACET have their hash indexes. 

After we have all the waypoints in the hash table, it is very fast to insert new waypoints. To build the hash 

table, the NAS is divided into grids to build the hash pair index, which is critical in a hash table data 

structure. Each grid is a square with four corners whose latitude and longitude are integers as shown in 

Figure 22, where i and j are integers. When we have a new waypoint to be inserted, we first calculate its 

hash key pair and decide which grid it is in. As we use floor function to obtain <lat, lon> from the 

absolute latitude and longitude, if the waypoint exists, it should be in the grid with top-left corner 

coordinates (i, j) = <lat, lon>. Therefore only the existing waypoints ଵǡ ଶǡ ଷ in this grid need to 

be checked instead of checking all the waypoints in the entire Jetway Network. This mechanism for the 

hash table substantially enhances the speed with which new waypoints are inserted. 

Figure 22: Hash table indexed grids with waypoints 

The grids in the hash table are stored with sorted hash key pair as shown in Table 4. The grids are saved 

in ascending order of the first integer of the hash key pair, and with the same first integer, the grids are 
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recorded in ascending order of the second integer of the hash key pair. The waypoints in each grid are 

recorded under the hash key pair <lat, lon> which is also actually the top-left coordinates (i, j) of the 

corresponding grid. If there is no waypoint in a grid, that grid will not be stored in the hash table. 

Table 4: Hash table is stored with ordering 

... ... 

<lat_(i-1), lon_(j-1)> wp4, wp8, wp12 

<lat_(i-1), lon_(j)> wp1, wp2, wp3, wp7, wp11 

<lat_(i-1), lon_(j+1)> wp5, wp6, wp9, wp10 

... ... 

<lat_(i), lon_(k-1)> wp44, wp18, wp32, wp11, wp20 

<lat_(i), lon_(k)> wp31, wp12, wp53, wp47 

<lat_(i), lon_(k+1)> wp65, wp26, wp19 

... ... 

4.3.3.4. Establish the’ Jetway Plus’ Network

After generating the Jetway Network, the routes for input flight plans of flexible flights are optimized. 

Since the waypoints ordered by FACET may not be complete, the new waypoints mentioned in the input 

flight plans need to be imported to the Jetway Network. At the same time, all the airports are imported by 

treating them as waypoints as well. The additional edges are obtained from FACET by the historic flight 

track files. Together all the additional inserted elements with the Jetway Network formulate the ‘Jetway 

Plus’ Network, which is the final data structure to apply our routing algorithm. The entire process is 

exemplified in the following set of steps. 

1) Input Flight Plans

Flight plans are documents filed by pilots or a flight dispatcher with the local Civil Aviation Authority 

(e.g. FAA in the USA) prior to departure. They generally include basic information such as departure and 

arrival points, the sequence of waypoints of the flight route, estimated time en route, alternate airports in 

case of bad weather, type of flight (whether instrument flight rules or visual flight rules), pilot's name and 

number of people on board. 

2) Obtain and insert new waypoints from the input flight plans 

From the input flight plans we obtain the sequence of waypoints of the flight route and insert the 

waypoints into the hashed Jetway Network. The related edges are also added. For each waypoint, we 

obtain its latitude-longitude pair and search for it in the existing Jetway Network hash table. If this 

waypoint exists, we move on to the next current flight plan; if this waypoint is new, it is inserted into the 

hash table. After all of the flight plans are processed and all the airports are inserted, the ‘Jetway Plus’ 

Network is formulated. 

3) Flexible flight routing algorithms 

A. Shortest path algorithm without sector congestion constraint 

The waypoint-based ‘Jetway Plus’ Network obtained above is the routing data structure for the entire 

NAS. When the routing algorithm does not take the sector congestion into account, the weight of each 

edge is the great circle distance between two waypoints. The shortest path algorithm is used to find the 

optimal route (shortest weighted path) from origin to destination for each pair of origin/destination. Since 
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the flexible flights are considered, the destination airport of each flight is selected from the airports inside 

its destination metroplex. In this research, the central airport of a metroplex is chosen as the destination of 

a flexible flight, determined by calculating the central point c of the airports in the Metroplex and then 

selecting the closest airport to that point. For example, the N90 metroplex has three major airports: JFK, 

LGA, and EWR. After the central point calculation is made, LGA is identified as closest to that point. 

B. Shortest path algorithm with sector congestion constraint 

To introduce sector congestion constraints, weights on edges that intersect congested sectors are increased. 

Therefore, the resulting shortest path route for a flight after Dijkstra’s Algorithm is employed represents 

the shortest weather conflict free route from origin to destination. For advanced multiple-level sector 

congestion, the sector congestion impact is taken into account by assigning the edges with additional 

weights of different congestion statuses. The more congested is a sector, the heavier weight on each 

intersecting edge. The congestion weight will be summed up with the great circle distance weight for each 

edge and then the shortest path algorithm is applied in this new weighted ‘Jetway Plus’ Network. 

In sum, our routing algorithm calculates the optimal path between origin and destination airports. At the 

same time, LTV can balance the traffic load to efficiently avoid the congested areas (Figure 23). The 

optimized flight plan is generated under the sector congestion constraints and is compared to its original 

flight plan. Assume that the congested sector is set to be the dark area in Figure 23 because of bad 

weather. The origin airport is LAX and the destination metroplex is NY90 whose central airport is LGA. 

The weighting scheme is on-off weighting and Ȗ ൌ ͳͲ. The result shows that the optimized route 

successfully avoids the congested area. 

Figure 23: The original and optimized routes for a flexible flight 

4.3.4. Estimation of all possible routes of a flexible flight 

There are no flexible flights defined in the historical data. As a result, for each of the FFS-designated 

flexible flights, there is only one flight plan in the historical data. To compare among a set of alternate 
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flight plans whose destinations cover all the candidate airports inside the metroplex, these alternate flight 

plans should be estimated. The process employed to do this is explained next via example. 

Given a set of selected flexible flights, for the i-th flexible flight from ORD to JFK, whose model type is 

B747 and the weight category is “H”, we estimate the routes from ORD to EWR and LGA based on the 

historical data. For different situations, we use different estimation strategies, as follows. 

x Case 1. Same Aircraft Type 

We search for B747 from ORD to EWR inside the historical flight plans. In Figure 24, the route in black 

is the original route to JFK, the route in maroon is another B747’s route to EWR and wp_com is the last 

common waypoint of the two routes. This route is used for ORD-EWR flexible flights. 

Figure 24: Original Routes of B747 to JFK and EWR 

x Case 2. Same Weight Category 

If no B747 (same aircraft type) found, we search for the flight with similar type (i.e. the same weight 

category “H”) and then employ same procedure as Case 1. 

x Case 3. No same type or same weight category 

Sometimes there is only one daily flight from a small airport to JFK. To estimate the ETAs of EWR and 

its arrival fix 40002, we use the ratio of the distance method as shown in Figure 25.

400 _ 1

_

02

1 2

wp com

E R W wp com

ETA

E

ETA t

TA ETA t t

 

 
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Figure 25: Virtual route of not existing city pairs 

4.3.5. Final LTV output data sets 

There are 4 sets of output data for N90 metroplex (same four for SCT). 

x Data Set 1 – the historical data (Nov 7, 2008) 

x Data Set 2 – the optimized data (LTV is applied on Data Set 1) 

x Data Set 3 – the historical data + flexible flight operations (No optimization is applied) 

x Data Set 4 – the historical data + flexible flight operations + LTV optimization 

The output files contain the detailed flight plans including arriving destination airports and corresponding 

arrival fixes. Figure 26 depicts the example output format. The columns are AirlineID, CID, weight 

category, the arrival fix passing by, ETAs sorted by ascending time order, altitude at 40002, airspeed at 

40002, the sequence of O/D airports and waypoints. 

Figure 26: The example of output format of LTV 

Data Set 3 is the output of applying flexible flights to historical data. There are four steps to generate Data 

Set 3 (see steps below and Figure 27). 

x Step 1 – FFS tells LTV which flights are designated as flexible flights. 
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x Step 2 – Based on Data Set 1, we estimate all possible routes and ETAs for each flexible flight. 

Now each flexible flight has three destination ETAs for EWR, JFK and LGA. 

x Step 3 – At the same time, LTV passes all the regular flights to McTMA, which provides the 

runway balancer available time slots (holes) at each airport. 

x Step 4 – Based on the estimated three ETAs and the available slots, the runway balancer decides 

which airport each flexible flight is going to land in. Then each flexible flight has one single 

destination ETA and only one flight plan. 

Figure 27: Data flow diagram of the entire system 

The procedure of processing Data Set 4 is similar as Data Set 3 except that in Data Set 3 we deal with 

historical data while in Data Set 4 we deal with the optimized data. 

4.4. Multi-center Traffic Management Advisor Model (McTMA) 

For this project, a model of the Multi-center Traffic Management Advisor scheduling system (6) was 

adapted for the N90 and SCT airspaces. The model, originally in Matlab code, was transferred to Java 

code to enable future portability to the Aircraft Concept Evaluation System (ACES) or other systems. 

Figure 28 depicts the scheduling system, which consists primarily of a number of scheduling entities that 

communicate between themselves using a publish/subscribe mechanism. An external module that assigns 

flexible flights to airport-runway combinations was also developed and is considered part of the 

scheduling system, although it is a separate module. The input to the system are estimated times of arrival 

(ETAs), determined by the LTV, which in turn uses ETAs generated by ACES. 

ACES
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Figure 28: Multi-center Traffic Management Advisor model schematic 

Each scheduler instance takes as input ETAs to the “meter point” at which it is scheduling, and applies 

local separation constraints. Specifically, the scheduler ensures that all aircraft crossing the meter point 

have appropriate lateral separation, which is 5 nautical miles (nmi), by assigning required times of arrival 

(RTAs) that equate to 5 nmi assuming the aircraft are flying the modeled groundspeed at the meter point. 

(As such, all aircraft are de-conflicted over the meter point.) The scheduler that schedules the airport 

runways and meter fixes ensures that wake vortex separation is met at the runway and that proper lateral 

separation is obtained at the meter fixes. No airport arrival rates were set. 

In addition, when assigning the RTAs, the schedulers ensure that there is sufficient capacity downstream 

to allow each aircraft being delivered from the meter point to arrive at the downstream resource without 

excessive delay. Specifically, each resource (scheduler) computes a “rate profile,” which is an estimate of 

the capacity assigned or available to an upstream resource (scheduler). A scheduler will delay an aircraft 

in excess of that required to meet lateral separation so as to be able to arrive at all downstream resources 

at times where assigned or otherwise available capacity exists. Once scheduling is complete, the scheduler 

computes a rate profile for each of the other resources which are delivering aircraft to the scheduler and 

publishes those rate profiles. The scheduler also publishes its RTAs. 

The scheduler would ideally re-compute schedules whenever new information is obtained. In the actual 

McTMA system, this is done at least as frequently as radar updates are obtained, which is approximately 

every 12 seconds. In this system, the scheduler is only run for one full cycle. (As will be discussed, this 

involves two scheduler runs.) Additional details about the scheduler can be found in Landry (6). For this 

project, the scheduler was ported to JAVA code and adapted for scheduling arrivals in to N90 and SCT. 

In addition, an “airport-runway balancer” (ARB) was developed, and the scheduler was integrated with 

the ARB and LTV. 

4.4.1. Airport-runway balancer (ARB) 

Typically, the input to the McTMA scheduler includes the destination of the aircraft. Runways are 

assigned based on the current configuration of the destination airport. However, under the “flexible flights” 

Publish/Subscribe

Scheduler

T1

Scheduler

Y

Scheduler

X

Scheduler

Z

Rate profiles

RTAs

ETAs

Rate profiles

ETAs

Rate profiles

ETAs

Rate profiles

ETAs

Rate profiles

Rate profiles

RTAs

Rate profiles

RTAs

LTV

ETAs

RTAs

ACES

ACES

Rate profiles

(from 

schedule 

using nonflexible 

flights only)

Airport-runway balancer
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concept, some aircraft would have destinations that are non-specific with respect to the airport. An 

algorithm for assigning aircraft to airports had to be added to the McTMA model. 

Algorithms for this purpose were developed as part of ACES, for which we could find no documentation, 

and for the Final Approach Spacing Tool (FAST), which is part of NASA’s Center-TRACON 

Automation System. Neither algorithm was adequate for the task; for example, the assignment algorithm 

in FAST utilized a complex rule-base that was highly adapted to the particular airport. A simpler and 

more general method that still produced near-optimal allocations was desired. 

The general algorithm for the ARB proceeds as follows: 

x run the scheduler on all airport-bound flights only, without the flexible flights; 

x compute the rate profiles at the runways for all airports in the metroplex; 

x pass the available capacity (“holes”) at those airports to the ARB along with the ETAs of all 

flexible flights to each of the airport-runway combinations; 

x ARB assigns aircraft in a first-come, first-served fashion to the available holes; 

x aircraft are assigned the airport-runway combination associated with the hole to which they are 

assigned; and 

x the assignments are passed to the scheduler, which re-computes the schedule to include flexible 

flights.

The process can be demonstrated using the example shown in Figure 29. In Figure 29, the holes computed 

by the scheduler are shown in the column labeled “holes.” For example, there is available capacity for an 

aircraft at 1226, 1228, and 1235 at LaGuardia (LGA) runway 04, and capacity available at Kennedy (JFK) 

runway 13L at 1230 and 1234. 

The aircraft ETAs to JFK and LGA are shown in the middle and right columns. (The other airports in the 

metroplex are not shown to improve readability.) The ARB accepts the holes and ETAs as inputs. 

Starting from the first hole, 1226 at LGA-04, the ARB looks for the next flight arriving at LGA. That 

flight is DAL1217, which can arrive at LGA at 1226. Since it can arrive at or before 1226, it could utilize 

the 1226 LGA-04 hole, and it is therefore assigned to that airport and runway. DAL1217 is then removed 

from further consideration by the ARB, which is accomplished by removing its ETA from all lists. 

The next hole, also at LGA-04, is at 1228. The next LGA ETA is UAL42, which arrives at 1228 and is 

assigned LGA-04. UAL42 is then removed from all ETA lists. This continues until all aircraft are 

assigned airport-runway combinations. 
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Figure 29: ARB example 

The assignment of a hole confers only an airport-runway assignment, it does not confer a particular 

scheduled time of arrival. The scheduler, run subsequently to the ARB, may assign the aircraft a different 

RTA than was used by the ARB to assign an airport-runway to the aircraft. 

4.5. How the models were linked 

The following description and Figure 30 summarize how the inputs/outputs from the models were linked. 

This linkage was done manually, but future work may warrant automating this procedure. The LTV 

accepts ETA and routing information from an ACES run in the form of a static input file. ACES provides 

the historical flight plan data with Airline ID, computer ID, aircraft type, weight category, origin airport, 

destination airport, the sequence of waypoints and the ETAs, altitude, airspeed along the trajectory. LTV 

then optimizes the route according to the method described previously. LTV outputs the ETAs to all 

meter points, meter fixes, and the airport runway thresholds. 

Figure 30: ACES/LTV/McTMA model interactions 

Holes

1235 – LGA-04

1234 – JFK-13L

1233 – EWR-04L

1230 – JFK-13L

1228 – LGA-04

1226 – LGA-04

JFK ETAs

1236 – BAW35

1235 – AAL13

1233 – CHQ410

1231 – UAL42

1231 – DAL1217

1230 – AAL440

LGA ETAs

1238 – BAW35

1234 – USA1200

1234 – AAL13

1230 – AAL440

1228 – UAL42

1226 – DAL1217

DAL1217 LGA-04

UAL42 LGA-04

AAL440 JFK-13L

LTV

McTMA

Scheduler

ARB

ETAs 

Airport/Runway 

assignments for 

flexible flights

Holes (nonflexible flights)

(non-flexible 

flights only)

RTAs

ACES

ETAs 
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Other necessary information for each flight such as aircraft type, altitude, ground speed, and route are also 

output to the McTMA scheduler so that it can be used to estimate the wake vortex separation between 

aircraft and perform other calculations. That output is accepted as input by the scheduler, which operates 

as described above. In essence, McTMA takes the optimized regular flights and runs the scheduler to find 

out the available time slots (holes) for the flexible flights. These holes are then sent to Runway Balancer 

that will decide which destination airport a flexible flight should land in. Now every flexible flight only 

has one destination airport and one flight plan.

4.6. Airspace Concepts Evaluation System (ACES) 

Airspace Concepts Evaluation System (ACES) is a fast-time simulation of the nationwide air traffic 

system, including air traffic management (ATM), flight, and airline operational control (AOC) functions 

(7). The Airspace Concept Evaluation System (ACES) was developed under the Virtual Airspace 

Simulation Technologies element of the VAMS Project at NASA Ames Research Center, and 

development continued under a subcontract with Raytheon Corporation through May of 2011. During the 

development period, Intelligent Automation, Inc. was one of the subcontractors responsible for ACES 

software development. ACES (summarized in Figure 31) enables comprehensive assessment of the 

impact of new tools, concepts, and architectures, including those that represent a revolutionary change in 

current NAS operations. ACES is composed of interoperable models that represent the gate-to-gate 

actions and highly coupled interactions between key participants within the National Airspace System. 

Figure 31: High Level System Diagram of ACES 

TRACON

Airport

Center A Center B

TRACON

Airport

ACES Simulation

ATC

ATC

ATC

ATC ATC

ATC

TFM

TFM

TFM

TFM TFM
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The core components of ACES infrastructure consist of: 

1. The simulation agents and containers that model the current National Airspace System (NAS) 

2. CybelePro, the simulation infrastructure that provides a publish/subscribe communications 

mechanism with an agent-based modeling and simulation framework 

3. The Multiple Run Manager, which provides a batch mode and interactive run-time interface 

4. Visualization and scenario control tools 

5. Message and internal data collection and logging functionality 

6. Performance monitoring tools 

ACES includes models of following air traffic management functions: 

x Airport- Traffic Flow Management (TFM) 

x Airport- Air Traffic Control (ATC)

x Terminal Radar Approach Control (TRACON) TFM 

x TRACON ATC

x Air Route Traffic Control Center (ARTCC) TFM 

x ARTCC ATC 

x Air Traffic Control System Command Center (ATCSCC) TFM 

x Airline Operations Center (AOC) and Weather 

x Traffic Management Advisor (TMA) 

x Communication, Navigation and Surveillance (CNS) 

The current ACES design instantiates:

x As many flight agents as specified in the traffic data set 

x 21 pairs of ARTCC TFM and ATC agents, one pair for each domestic ARTCC, and 1 pair for the 

international airspace 

x Airport TFM and ATC and TRACON TFM and ATC agents, where a set of these four agents is 

constructed as required to fly the flights 

x One ATCSCC TFM agent,

x 18 AOC agents (for traffic demand generation), and 

x A trajectory generator model, parameterized for different aircraft types, used to construct 

trajectories individually for each flight 

ACES performs a gate-to-gate simulation of each flight. Flight movement is modeled from the departure 

airport terminal gate, through the departure airport surface taxi system to and through the takeoff runway 

system, through the terminal airspace to a departure fix, through a series of en route ARTCCs and their 

sectors to an arrival fix on a terminal airspace boundary, through the terminal airspace to and through the 

landing runway system, and through the arrival airport surface taxi system to the arrival airport terminal 

gate. ACES TFM models develop traffic flow plans and issue traffic restrictions and advisories to each 

other and ATC agents. ACES ATC models manage flight movement and apply operating procedures 

subject to satisfaction of the TFM traffic restrictions. These operating procedures are based upon common 

standard operating procedures within the Centers in the NAS. These simulations are driven by input data 

describing the arrival and departure schedule by airport and flight plan for each flight. ACES provides 

various levels of modeling fidelity. En route operations generally are simulated with higher fidelity than 

terminal. An aircraft trajectory model is used to simulate detailed, time-stepped flight dynamics in en 

route airspace, whereas node-to-node transit times are used to simulate terminal flight movement. The 

boundaries of the 20 domestic ARTCCs and all en route sectors are encoded in ACES databases. 
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4.7. Demand / Business Model 

4.7.1. Introduction 

Before presenting the results of the analysis models and tools presented in the above portions of Section 4, 

in this part of the report, we discuss the economics of flexible flights with regard to all three markets—

on-demand, general aviation, and commercial operators. First we discuss the various data sources and 

how we processed them. Next we apply this data to several questions: (1) if commercial airlines were to 

use the flexible flight option, what markets are they most likely to target? (2) What are the differences in 

implementing flexible flights for commercial vs. on demand users? (3) From a commercial passenger 

perspective, what ticket price reductions (if any) would they expect when purchasing a metroplex-bound 

flight option? (4) From a passenger perspective, what is the total cost (airline fare + ground transportation) 

of traveling to their ultimate destination (a downtown location, for example)? This final question includes 

the more general concept of intermodal transportation, where we model the transit time from airport to 

downtown as an additional delay in the traveler’s itinerary. 

4.7.2. Airline Origin and Destination Survey (DB1B) 

The primary data source for the study is Airline Origin and Destination Survey (DB1B) provided by the 

Bureau of Transportation Statistics (8). DB1B is a 10% sample of all airline tickets reported by airlines to 

the Office of Airline Information of the Bureau of Transportation Statics. There are three table categories 

available in DB1B: Coupon, Market and Ticket. This study uses only Coupon and Market tables. Data 

from these tables help in investigating traffic patterns, airline market share, passenger flow and some 

information on ticket pricing. The survey contains quarterly data from 1994 to 2011. 

DB1B Coupon 

The table provides information about each domestic itinerary of the Origin and Destination survey, 

including operating carrier, origin, and destination, number of passengers, fare class, trip break and 

distance. Appendix A has more information on this table. 

DB1B Market 

This table contains the Origin-Destination specific characteristics of each domestic itinerary of the Origin 

and Destination survey, including reporting carrier, origin and destination, prorated market fare, number 

of coupons, and miles flown. Appendix A has more information on this table. 

DB1B Ticket 

This table consists of summary characteristics of each itinerary. Data on carrier, fare, number of 

passengers, origin airport, roundtrip indication and miles flown are reported. 

4.7.3. Combining DB1B Database Tables 

The Coupon and Market tables report different fields of the same airline reported data set. The tables 

contain a few common fields that associate databases with each other. Table 5 shows some of the fields 

reported by database. The field “Coupons” refers to individual legs of a journey that is either direct or part 
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of a connecting itinerary. “Market Fare” is the total amount paid for OD journey. “Fare Class” identifies 

the tickets as economy or business class. 

Table 5: DB1B Coupon and Market Fields Comparison 

Field COUPON MARKET 

Itinerary ID 9 9

Market ID 9 9

Year 9 9

Quarter 9 9

Sequence No. 9 8

Coupons 9 9

No. of Passengers 9 9

Fare Class 9 8

Market Fare 8 9

Distance 9 9

Origin 9 9

Destination 9 9

Carrier 9 9

This study uses the common field ‘Itinerary ID’ to merge the two databases. Itinerary ID is a set of unique 

identifiers for each coupon and OD pair. The field ‘Market Fare is reported’ is reported only in Market 

table while Fare Class is unique to Coupon only. The merged database helps in identifying the number of 

stops made, ticket price for each leg, market share of each airline etc. 

4.7.4. DB1B Data Interpretation 

The merged data from the tables above provides all the information about the OD travel. Consider the 

itinerary ID 2004219. Table 6 to Table 8 show the data snippets from Market and Coupon tables. Within 

the Coupon database there are three entries with the same itinerary ID 2004219, these are three segments 

of one round trip between ABQ and BOS. The three entries belong to the same Fare Class ‘X’ or 

economy. Figure 32 shows the three entries on the map. The table shows the exact movement of the 

passenger who was on this trip; without any other information it would be difficult to ascertain if the 

destination was BOS or ABQ. Also, Coupon table also does not report any ticket price information which 

is very important for any demand-fare modeling. 

Table 6 : Coupon Table for Itinerary 2004219 

Market table helps in determining ticket price. There are two entries within the Market database with the 

itinerary ID 2004219 – the first is from ABQ to BOS and second from BOS to ABQ. This shows that this 

is a round trip from ABQ to BOS. Market table also shows that the ticket price paid for each way was 

$207.47 and $208.62 respectively. Market table does not break down the journey into segments like 

Coupon does and therefore ticket price data for each segment is not reported. Merged tables provide 

ID Itinerary ID Market ID Seq No. Coupons Year Quarter Origin Destination Carrier PAX Fare Class Dist

186440 2004219 2004236 1 3 2004 2 ABQ BOS UA 1 X 1974

186441 2004219 2004219 2 3 2004 2 BOS ORD UA 1 X 867

186442 2004219 2004219 3 3 2004 2 ORD ABQ UA 1 X 1118
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complete data including ticket price on each segment. 

Table 7: Market Table for Itinerary 2004219 

DB1B Market reports prorated ticket price based on miles flown; i.e. the ratio between ticket price and 

total distance flown can provide the ticket price for all the segments involved. The merged table shown in 

Table 8 represents the ticket fare for each segment of the return leg of the journey. 

Table 8: Merged Coupon and Market Tables for Itinerary 2004219 

Figure 32: Coupon Table results, three entries1



1 Image from Great Circle Mapper, http://www.gcmap.com/

ID Itinerary_ID Market_ID Year Quarter Origin Destination PAX Dist Market Fare

2480259 2004219 2004236 2004 2 ABQ BOS 1 1974 207.47

2480260 2004219 2004237 2004 2 BOS ABQ 1 1974 208.62

ID Itinerary ID Market ID Seq No. Coupons Year Quarter Origin Destination Carrier PAX Fare Class Dist Fare

186440 2004219 2004236 1 3 2004 2 ABQ BOS UA 1 X 1974 207.47

186441 2004219 2004219 2 3 2004 2 BOS OR UA 1 X 867 91.12

186442 2004219 2004219 3 3 2004 2 ORD ABQ UA 1 X 1118 117.5

SEQ-1 ABQ - BOS

ITIN_ID = 2004219

MARKET_ID=2004236

COACH

SEQ-2 BOS-ORD

ITIN_ID = 2004219

MARKET_ID=2004237

SEQ-3 ORD-ABQ COACH

ITIN_ID = 2004219

MARKET_ID=2004237

COACH
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Figure 33: Market Table results, two entries2

This study requires OD data for each passenger. Market tables reports all required fields except fare class, 

which is obtained from coupon table. 

4.7.5. On-Time performance data 

4.7.5.1. TǦ100 Tables

This table contains on-time arrival and departure data for all non-stop domestic flights by major carriers. 

It provides departure and arrival delays, origin and destination, and scheduled and actual arrival times. 

On-time performance data helps in constructing statistical models that predict arrival delays at each 

airport.

Figure 34 and Figure 35 show arrival delays at JFK during 2009 Q1. Erlang and Poisson distribution 

provide a good fit for the data and can be used instead for future analyses. 



2 Image from Great Circle Mapper, http://www.gcmap.com/

SEQ-2 ABQ - BOS

ITIN_ID = 2004219

MARKET_ID=2004237

FARE = $208.62

SEQ-1 ABQ - BOS

ITIN_ID = 2004219

MARKET_ID=2004236

FARE = $207.47
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Figure 34: Erlang distribution, JFK arrival delays for 2009, Q1 

Figure 35: Poisson distribution, JFK arrival delays for 2009, Q1 

-100 -50 0 50 100 150 200 250

0

0.02

0.04

0.06

0.08

0.1

0.12

Delay (min)

Density

Arrival Delays - JFK, ERLANG DIST, R2 = 0.97845

Regression

Data

-100 -50 0 50 100 150 200 250

0

0.02

0.04

0.06

0.08

0.1

0.12

Delay (min)

Density

Arrival Delays - JFK, POISSON DIST, R2 = 0.97179

Regression

Data
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4.7.6. Metroplex Markets 

(Note: Equations are numbered in this sections 4.7.6 – 4.7.8 since there are several) 

4.7.6.1. Schedule padding

FAA designates flights as “late” if they are delayed by 15 minutes or over. Airlines are required to report 

all late flights and the data is made public under T-100 On-Time performance tables. Late flights not only 

hurt the airline’s reputation among passengers but also increase operational costs with respect to fuel, 

cabin crew salaries etc. Total delay costs for U.S. Airlines for 2008 was $6.1 billion (9). Delays caused by 

congested airports are unavoidable; using years of experience of operating flights on the same routes, 

airlines deliberately over-estimate the flight duration so that most of the time they arrive at their 

destination at or before the scheduled arrival time. This practice is commonly referred to as schedule 

padding and it allows airlines to report fewer late flights, even in the presence of congestion at the arrival 

airports. The frequency and duration of this extra flight time remains hidden as the actual flight times are 

never reported to Department of Transport (DOT). With the increasing congestion at airports over the past 

several years, it was thought that by analyzing flights operated by the same airlines using same aircraft 

and same departure time, but separated by ten years, the extent of schedule padding could be estimated. 

Figure 36 were generated using T-100 table for the year 1999 and 2009 with the same carrier, origindestination and departure time. The analysis shows that there are flights where schedule padding leads to 

an additional 15 to 30 minutes to the total flight duration. 

Figure 36: Schedule Padding at N90, 2009 Q1 

4.7.6.2. Target Metroplex Markets

Metroplex operations could lead to lower airline delays on flight duration for all flights, irrespective of 

the total distance and type of aircraft. The ratio between flight delay and flight duration (Normalized 

Delay) is a good metric to evaluate the impact of new concepts. A higher ratio indicates more value of 

such operations to passengers; for example, a 20 minute delay on a 30 minute flight is worse than on a 

200 minute flight. Based on this rationale, metroplex operations would have greater appeal to passengers 

on short duration flights. 

12 AM 3 AM 6 AM 9 AM 12 PM 3PM 6 PM 9 PM 0

50

100

150

200

250

Flights with padding of between 0 and 15 min

Time of Arrival

# Flights

12 AM 3 AM 6 AM 9 AM 12 PM 3PM 6 PM 9 PM 0

20

40

60

80

100

120

Flights with padding of between 15 and 30 min

Time of Arrival

# Flights

Peaks 

around rush 

hour
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Figure 37 shows the number of passengers arriving at N90 from around the country. The figure on the 

right shows the arrival market ordered with respect to their distances from N90. The figure also identifies 

markets that are closer than 800 miles as potential metroplex market based on Normalized Delay. 

Figure 37: Passengers Arriving at N90 metroplex, Q1 2009 

Normalized Delay does not consider two important features that would affect passengers’ choice - 

purpose of travel and economic demography. Travelers who value time more than money would most 

likely choose to avoid any deviation from their intended travel plan at any cost and therefore would avoid 

metroplex flights. Others, who value money more than time, would opt for metroplex flights in return for 

lower ticket prices, even if their travel time would be longer, to include ground transportation. Passengers 

who in general are looking for a cheaper ticket would opt for metroplex flights. Parameters such as group 

bookings, ticket class, and travel time could estimate a passenger’s level of attraction to a metroplex 

flight. Passengers traveling in a group and looking for a cheap ticket are assumed to be traveling for 

leisure, and therefore would be interested in metroplex flights. Passengers traveling on a short journey are 

more likely to buy a metroplex ticket, based upon higher Normalized Delays. 

Using the parameters above, we can identify a new metric metroplex Index (MP_index). It is defined as 

follows:

 

 Eq. 1 

Where

i, j = Origin airport i, destination airport j

MP_indexi,j = Metroplex cost metric for flights from origin i to destination j

60 80 70 90 100 110 120 130 30

40

50

0

0.5

1

1.5

2

x 104

Latitude

BOS

NewYork

DCA DTW

RDU CLT

ORD

ATL

MSP

MCO

PBI

MIA FLL

TPA

Longitude

PAX arriving at N90 - Q1 2009

DFW

IAH

DEN SEA

PHX

LAS

LAX

SFO

PAX

0 500 1000 1500 2000 2500 3000 0

0.5

1

1.5

2

2.5

3

x 104

PAX

Distance from N90 (mi)

PAX arriving at N90 - Q1 2009

ATL

BOS

CLT

DCA DEN

DFW

DTW

FLL

IAH

LAS

LAX

MCO

MIA

MSP

ORD

PBI

PHX RDU

SEA

SFO

TPA

AT i j

GT

i j total

coach

i j total

group

i j T

T

Tickets

Tickets

PAX

PAX

MP index

, , ,

, _  ̧

 ̧

1

·

 ̈

 ̈

©

§

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 ̧

1

·

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§

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1

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A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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PAXgroup, PAXtotal = Number of traveling passengers - in group and total 

Ticketscoach, Ticketstotal = Number of passengers – in coach and total 

TGT = Time of travel from destination airport to final destination (minutes) 

TAT = Time of travel in air (minutes) 

i = Origin Airport 

j = Destination Airport

TGT is an important parameter as it captures the effects of traffic on ground transport time from 

destination airport to the final in-city destination. Researchers are encouraged to use the best available 

estimates for this value based on the metroplex of interest. 

Publicly available data from DB1B Market database is sufficient to calculate MP_index for all U.S. 

Domestic markets. If the assumptions are true, higher MP_index value indicates better suitability for 

metroplex flight operations.

Figure 38 compares markets connecting Operational Evolution Partnership (OEP) airports with N90 

airports using MP_index assuming the final destination as Times Square, NY. The ground transportation 

time from the airport to the final destination is shown in Figure 38. 

 Figure 38: Potential Metroplex Markets Comparison 

0

0.05

0.1

0.15

0.2

0.25

0.3

BOSͲLGA

DCAͲLGA

BOSͲJFK

BOSͲEWR

PITͲJFK

IADͲJFK

PITͲLGA

CLEͲLGA

DCAͲJFK

DTWͲLGA

PITͲEWR

MCOͲJFK

FLLͲLGA

FLLͲJFK

TPAͲJFK

MCOͲLGA

ORDͲJFK

CLTͲJFK

CLEͲEWR

TPAͲLGA

ATLͲLGA

MIAͲLGA

STLͲLGA

ORDͲLGA

MCOͲEWR

MP_index

Top 25 Metroplex Routes

2009 Q1 data from DB1B Coupon tables

To: Times Square

From TGT (min)

EWR 18

JFK 28

LGA 26
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The analysis shows short range markets between Boston and N90 are some of the key markets for 

metroplex operations. The short range markets dominate the top spots but the overall list has a mix of 

both short and long routes like Pittsburgh – LaGuardia. Since MP_index gives equal importance to 

number of passengers in a group, ticket prices, and travel time, it could lead to biased results if one of 

these factors is dominant. If more data is available MP_index could be scaled by adding “weights” a, b, c 

to the original formulation as shown in equation 2. 

Eq. 2 

Where:

MP_indexi,j = Metroplex cost metric for flights from origin i to destination j

PAXgroup, PAXtotal = Number of traveling passengers - in group and total 

Ticketscoach, Ticketstotal = Number of passengers – in coach and total 

TGT = Time of travel from destination airport to final destination (min) 

TAT = Time of travel in air (min) 

i = Origin Airport 

j = Destination Airport

4.7.7. Metroplex Airline Models 

A ‘Metroplex Airline model’ could be based on three existing models – Traditional airlines, On-demand 

airlines and restricted All-Business airline. The All-business airline model is a recent concept that 

redesigns the interiors of a medium sized aircraft such as Boeing 757 that now accommodates only 

business class passengers. One such airline in operation is Openskies, a British Airways subsidiary that 

serves Paris – Newark and Paris – Dulles markets. The airline operates only 4 aircraft twice a week. Since 

this is a radical shift from traditional models and does not apply to the average passenger we do not 

consider it for metroplex operations.

Key areas of airline modeling are network design and flight and crew scheduling. The next few sections 

will present the existing modeling techniques for traditional and on-demand airlines along with possible 

methods to implement metroplex operations within them. 

4.7.7.1. Traditional Airlines

Traditional airlines typically employ hub and spoke networks to serve their markets. Hubs help in 

maximizing resource utilization by routing the majority of the flights through a few hub cities, thereby 

avoiding direct flight between airports with low demand. Hub and spoke structure helps airlines to 

maximize profits through the combination of cost economies and concentrated demand markets that are 

available at the hubs. Airlines are also able to build customer loyalty at the hubs by offering marketing 

devices such as frequent flyer programs, which are cost effective due to higher demand densities. 

AT i j

GT

i j total

coach

i j total

group

i j T

T

c

Tickets

Tickets b

PAX

PAX

MP index a

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, _  ̧

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1

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A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

43

The flight scheduling problem can be broken down into a sequence of steps: itinerary identification, fleet 

assignment, aircraft routing, and crew scheduling. The scheduling process begins with identifying the 

itinerary using extensive research on historical trends in air traffic. The itinerary for each potential 

passenger would include the origin, destination, departure time and approximate flight duration. This data 

provides schedule managers at the airline with targets that they try to meet with the existing infrastructure 

at their disposal. Estimated demands along with the number of aircraft available provide the flight 

frequency necessary throughout the day. 

The fleet assignment step aims at allocating aircraft on routes within performance constraints and to 

minimize operational costs. Some of the key operational constraints are airport runway lengths and 

aircraft fuel capacity. Fleet assignment for airlines based on hub and spoke network feeds several points 

of departure into a single hub airport from which connecting flights carry passengers to their destination. 

This assignment type can lead to congestion of traffic at the hub, causing further delays. At present, fleet 

assignment problems are formulated and solved as integer programming problems, however they turn 

intractable when solving large size problems (10). 

Each aircraft in the fleet has its own maintenance-routing plan and all routing plans need to be 

coordinated to maintain smooth operation at all times. Routing plans usually spanning two to three days 

and take the aircraft on a loop through the passengers’ demand destinations and through maintenance, 

stations. Certain stations are equipped with facilities and personnel to provide periodic mechanical checks 

which range from regular on-route service to a complete overhaul performed after every 6000 hours of 

service. A route planner’s job is to make sure the aircraft serves demand markets while fulfilling the 

servicing needs on time, and minimizing dead-head (non-revenue-generating) flights. Aircraft routing is a 

dynamic problem, one that constantly changes based on the health of aircraft in the fleet; in the event of a 

mechanical failure, the available resources need to be optimized so as to minimize delays and 

cancellations. The routing problem is solved after the fleet has been assigned. Traditionally, aircraft are 

assigned to each demand segment manually. 

Crew scheduling offers even more challenges than fleet scheduling because of the higher numbers of 

assignments and maximum hours per day, constraints that do not apply to aircraft. Crew pairing, crew 

rostering, and crew recovery are the main elements in crew scheduling problem. The crew pairing links 

two crew members with each other based on their experience and training; crew working limitations are 

mentioned in Federal Aviation Rules (FAR) work agreements. These FARs state that a flight crew may 

only work for a maximum of 40 hours during seven consecutive days. Crew rostering considers the 

problem of constructing monthly work schedules for each employee. Crew recovery involves rebuilding 

crew pairings in the event of a crew member being unavailable. 

4.7.7.1.1. Traditional Airline and Robustness

Air traffic is a highly dynamic system that is prone to fluctuations throughout the day and potential 

disruptions due to weather, congestion and technical / mechanical failures. Operating in such conditions, 

an airline’s ability to quickly reschedule flights in an efficient manner is most important. A robust airline 

schedule is flexible enough to recover in the event of unforeseen disruptions. In the event of a mechanical 

breakdown at the hub, the airline risks disrupting all other connecting flights, leading to a much bigger 

loss than the cancellation of just one flight. Clarke (11) summarizes major cause of flight delays at hubs 

as: weather (93%), aircraft equipment failure, runway unavailability, and excess volume. Airline schedule 

planning has some built-in slack to counter some of these uncertainties, but it flies against the objective of 

maximizing resource utilization. Excess slack would increase robustness but would also result in lower 

flight frequencies, which could result in lower revenues. 
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Flight cancellations have the biggest impact on the operations of a traditional airline based on hub and 

spoke network as it has the potential to cause widespread disruption to other flights and passengers 

downstream. In the event of a flight cancellation, the airline bears the additional cost of transporting the 

stranded passengers to their original intended destination, if necessary through another airline, and if no 

other alternatives exist, then the airline must also pay the cost of boarding. 

To increase robustness, Weide (12) suggests that, when scheduling, connecting flights with short slack 

time should use the same flight crew and aircraft as an added constraint. These flights form the most 

vulnerable part of the airline network, as any delay or cancellation would leave too little time for the 

airline to move reserve resources to that airport. By using the same crew on such flights, the need to 

identify and deploy a replacement aircraft or crew is avoided. Ahmad Beygi (2008)(13) suggests 

optimizing the entire airline schedule to channel excess slack from other airports to the one where it is 

needed in case of delay or cancellation. The optimizer would reschedule the flights while trying to 

minimize the changes to the original schedule, by giving enough slack to the flight scheduled to depart 

right after the delayed or cancelled flight. Rosenberg’s (2004)(14) concept involves isolating the delayed 

or cancelled flight from the hub traffic, thereby minimizing its effects on the rest of the network. Shan 

Lan (2002)(15)(16) suggests an increase in the frequency at critical airports so that even in the case of a 

cancellation the stranded passengers would not have to wait long for the next flight. Ageeva (2000)(17) 

uses constraints in the scheduling algorithm which result in routes that makes it easier for other aircraft 

serving adjacent airports to serve the stranded passengers at an airport. These routes also facilitate a 

reserve aircraft getting back in the route loop of another aircraft that is unavailable. 

The literature review table shown in Table 9 shows some of the recent work on robust airline scheduling. 

Table 9: Robust Airline scheduling relevant work (15) 

Objectives

Min Cost Min delays/

disruptions

Ease of 

recovery Isolation of disruptions

Schedule Design Shan Lan et 

al. (2002)

Kang & 

Clarke

Fleet Assignment

Rosenber

ger, et al. 

Maintenance Routing Shan Lan et (2001)

al. (2002)

Ageeva & 

Clarke 

(2000)

Kang & 

Clarke

Shan Lan et 

al. (2002)

Crew Scheduling

Yen & Birge,

Schaefer, et al. 

(2001)

Chebalov & 

Klabjan
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4.7.7.1.2. Traditional Airline Modeling for Metroplex Operations

Metroplex operations would involve flights arriving at airports that may be different from the original 

flight plan. Operating under a current-day traditional network, any such metroplex flight would break the 

scheduled chain of flights that use the same aircraft, and leave passengers at the original destination 

airport without any service. This scenario is identical to a flight being cancelled due to a mechanical fault, 

and could use some of the “robust” methods discussed in the previous section. Increased flight frequency 

at metroplex airports would allow stranded connecting passengers to be served without long delays. 

Adjacent routes with maximum commonality would allow aircraft to switch between them to service 

passengers stranded due to a metroplex flight arriving at an airport different from the original flight plan.

Table 10 below shows an example itinerary of a metroplex bound flight within a traditional airline 

network. For metroplex-bound flights the destinations are shown as metroplexes (in bold) and not 

airports, as the final destination would not be known in advance. 

Table 10: Example itinerary of a MP-bound flight operating within Traditional airline network 

Modeling metroplex operations as a traditional hub and spoke airline has some drawbacks. The traditional 

airlines are not structured to deal well with unplanned disruptions or diversions. The techniques to handle 

such diversions exist, but they are used in exceptional circumstances. Metroplex flights would need these 

techniques on a regular basis, which would override the system. A new itinerary generation method is 

needed, one that is tailored for metroplex operations within a traditional airline network. 

4.7.7.2. OnǦDemand Airlines

With the availability of small and relatively cheap aircraft, on-demand airlines provide services to 

travelers who call in advance to schedule a flight. Flight scheduling is the most critical objective for any 

on-demand airline. An effective scheduling system constructs minimum cost pilot and jet itineraries for a 

Traditional Itinerary MP-Bound Itinerary

UAL, B737 UAL, B737

FN DEP DTIME DEST ATIME FN DEP DTIME DEST ATIME

100 SFO* 1600 JFK 2100 100 SFO* 1600 N90 2100

101 JFK 2230 ORD 0000 101 N90 2230 C90 0000

300 ORD 1300 PDX 1630 300 C90 1300 PDX 1630

300 PDX 1715 SAN 1815 301 PDX 1515 SAN 1815

410 SAN 1915 ORD 2215 410 SAN 1915 C90 2215

901 ORD 1310 JFK 1500 901 C90 1310 N90 1500

001 JFK 1600 SFO* 2100 001 N90 1600 SFO* 2100

*Maintenance Base
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set of accepted transportation requests. Airlines such as DayJet(18) began providing per-seat, on-demand 

air transportation serves for the southeast in 2007. The flight scheduler would employ optimized 

algorithms with the objective of minimizing operation costs and instances of dead-head flights. 

4.7.7.2.1. OnǦDemand Air Taxi Service Flight Generation

An example scheduler problem formulation is the ‘dial-a-flight’ problem (19). The key components of 

this problem are (a) an online accept/reject system to quickly inform passengers if their air transportation 

requests can be serviced and at what price, and (b) an off-line scheduling system to construct minimum 

cost pilot and jet itineraries for the next day once the reservation deadline has passed (20). Passengers 

would need to input their travel plans a couple of days before the flight date. The required inputs would 

include origin, destination, earliest time of departure and latest time of arrival time. The scheduler would 

then assign available aircraft and crew to meet passenger demands with a few operational constraints. 

Typically, aircraft and crew must return to home base at the end of the day or for crew switching. Flight 

segments can have at most one break 

4.7.7.2.2. OnǦdemand Airline Modeling for Metroplex Operations

Modeling metroplex operations based on on-demand airline would require frequent updates to the 

scheduler. The scheduler would aim at trying to serve all travel requests and minimize dead-head flights. 

The on-demand metroplex operation constraints would be similar to scheduled constraints of balancing 

the available aircraft and crew along with trips for regular maintenance. In addition to these constraints, 

on-demand airline would need the aircraft to return to its base airport to switch crew, and at the end of 

each day. The on-demand metroplex airline would need to re-optimize resources (aircraft, crew) 

allocation after a pre-defined time period (e.g. every two hours) to account for any metroplex flight 

diversions.

Metroplex operations needs an airline model that is robust to multiple disruptions. With small size and 

flexible schedule, the on-demand airline model is better prepared to handle disruptions. On-demand 

metroplex airlines would operate a small- to medium-range, nearly homogenous fleet of aircraft, thereby 

maintaining higher robustness through increased flight frequency following a metroplex flight diversion. 

Initial MP operations are bound to grow as a niche market with limited coverage, thus starting smaller 

with the On-Demand model would be the most suitable. 

4.7.8. Demand / Business model 

Implementation of metroplex operations by airlines would require it to be profitable. Airlines would only 

be able to make profits if the concept is able to attract new passengers to air travel or have passengers 

currently using commercial, general aviation, or air taxi, who switch to metroplex-bound flights. 

Typically, airlines use a variety of methods to attract passengers; these include incentives such as airline 

miles, free upgrades, and access to lounges at the airport. 

A statistical model estimates the ticket prices offered from existing airlines. 

4.7.8.1. Ticket Price Statistical Model

The model uses BTS DB1B data for the year 2009 Q1. Appendix A lists the data fields and their 

descriptions. The model uses multiple regression analysis to estimate the causal relationships between 

ticket price and the origin – destination pair with other explanatory variables. The model ignores the 
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effects of seasonal changes to passenger demand. 

4.7.8.1.1. Model Variables

Ticket Class: The ticket price depends on the ticket class. DOT defines seven ticket class categories as 

shown in the Table 11. Since the target market for metroplex airlines is mainly passengers that prefer 

cheaper tickets, the regression model uses data for restricted and unrestricted coach class only. 

Trip Distance: Trip distance does not have a strong correlation with ticket price, since the end of 

regulation (21); however it still remains a factor as it directly impacts operating costs. Since metroplex 

target markets are likely to be short range the model considers only flights that are 800 miles or shorter. 

Market Concentration: Market concentration impacts ticket price through level of competition. Ticket 

prices are lower for markets where competing airlines operate. The percentage of total seats offered by 

each airline measures the market concentration. 

Table 11: Ticket fare class categories 

For an OD (origin - destination) pair Herfindahl Index (HI)(22) measures the market concentration. 

 

Where

HI p p p p ij n ij

2 2

3

2

2

2

1   ...

 Eq. 3 

 ¦

A

ij a T s

1

 Eq. 4 

ij

n

nij T

s

p Eq. 5 

i = Origin 

j = Destination 

sa = Total number of seats available from i to j by airline a

Tij = Total seats available from i to j

A = Total number of airlines in operation from i to j

pnij = Percentage of seats from i to j offered by airline n

Code Ticket Class

C Unrestricted Business

D Restricted Business

F Unrestricted First

G Restricted First

U Unknown

X Restricted Coach

Y Unrestricted Coach
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BTS T-100 table provides the total number of seats offered by each airline on each route. HI index varies 

from 0 to 1 where 1 indicates monopoly. Two airlines with equal market share would lead to HI value of 

0.5. Any market having a Herfindahl Index greater than 0.4 is considered a highly concentrated market 

and less than 0.18 a less concentrated market. The higher the concentration, the more likely the fare will 

increase in that market segment. 

Passenger Demand: Demand for air travel affects ticket price following demand and supply principal. 

High demand with low capacity leads to higher ticket price, while low demand generally leads to low 

ticket price; however this may not be true with markets having high HI index. 

Low Cost carrier presence: As a general trend any presence of low cost carriers would reduce the average 

fare. These airlines serve passengers who travel coach and do not serve business travelers. The ticket 

price model identifies eight low cost carriers as reported by BTS for the year 2009. 

Origin-Destination Airport Size: The model divides all airports into four groups based on their sizes as 

defined by the FAA (23) – Large Hub, Medium Hub, Small Hub, and Non Hub. Those airports enplaning 

1 percent or more of the total are classified as large hubs; airports enplaning between 0.25 percent and 

0.99 percent of the total are classified as medium hubs; airports enplaning 0.05 percent to 0.24 percent of 

the total are small hubs; and airports enplaning less than 0.05 percent of the total are non-hub. 

Delay: One of the main benefits of metroplex flights is that they avoid arrivals at congested airports, thus 

reducing delays. Reduced delay would lead to lower flight operating costs and could also lead to cheaper 

fares. BTS On-time performance table provides the delay data. 

4.7.8.1.2. Ticket Price Regression Model

The general regression model is shown below: 

                ij ij ij ij ij i j ij ij f  d  p  h  lc  s  s  del r 0 1 2 3 4 5 6 7 ln E E ln E ln E ln E E E E Eq. 6 

, , , , , , , are regression coefficients

Residue value

Normalized average arrival delays with flight duration

Destination airportsize (1,2,3,4)

Origin airportsize (1,2,3,4)

Low cost airline presence (0 or1)

index of segment ij

number of coach PAX fromi to j

distance fromi to j

, origin and destination

fare from airport i to j

E0 E1 E 2 E3 E 4 E5 E6 E7

 

 

 

 

 

 

 

 

 

 

ij

ij

j

i

ij

ij

ij

ij

ij

r

del

s

s

lc

h HI

p

d

i j

f

Where

Table 12 below shows the variables sources and the conditioning steps taken to combine data from DB1B 

coupon and market tables with T-100 tables. The price model uses the data entries shown in bold font. To 
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ignore outliers and promotional ticket sales, the regression model ignores any trip with ticket price 

cheaper than $50. 

Table 12: Regression Model Data Conditioning 

Step Database PAX OD Pair MP Market Criteria Variable

1 DB1B (Coupon + Market) 9,657,090 7,709 Ͳ fare, PAX, s

2 TͲ100 14,5673,377 13,776 Ͳ HI, LC

3 DB1B (Coupon + Market) 3,652,072 4,815 Dist < 800mi Fare > $50 fare, PAX, s

4 TͲ100 81,587,282 8,838 Dist < 800mi HI, LC

5 TͲ100 (Step 4) Ԗ DB1B (Step

3) 2,659,455 3,251 Dist < 800mi Fare > $50 HI, LC

6 OnTime Ͳ 2,445 Dist < 800mi delay

The results for the ordinary least square regression analysis are shown in Table 13. The trip distance 

parameter d has a positive sign indicating a direct correlation with fare; i.e., fare increases as distance 

increases. The passenger demand parameter p has a positive coefficient as well. As passenger demand 

increases, congestion increases, which leads to increased indirect costs that result in increased fares. The 

HI index has a negative coefficient indicating decreasing fare prices with increasing competition. The 

negative coefficient indicates that the presence of any low cost carriers drives the ticket prices down. The 

airport size parameters have negative coefficients, which show that if the origin or destination is a hub, 

then the fare would go down. This is primarily due to increased competition between airlines at larger 

airports; with more options to choose from, passengers opt for the cheaper flights, in turn driving airlines 

to reduce ticket prices as well. The strong positive delay parameter shows that higher delays lead to 

higher ticket prices. Thus if metroplex flights reduce delays, the model would be able to estimate the new 

lower ticket price. The statistical analysis shows R squared value of 0.59 with low errors for all 

parameters. All parameters are statistically significant with p-value less than 1%. 

Table 13: Regression model results 

Variable Coefficient Standard Error

Intercept 0.7865 0.0700

ln(dij) 0.7111 0.0112

ln(pij) 0.0248 0.0038

ln(hij) Ͳ0.0252 0.0081

lcij Ͳ0.7223 0.0321

s

i Ͳ0.0558 0.0075

s

j Ͳ0.0685 0.0074

delij 0.2100 0.0535
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R squared 0.5931

# Observations 1,527,600

Before applying the regression model to predict ticket prices it is important note that due to the three 

different data sources – DB1B, T-100 and On-time performance tables complete data on only 2,445 OD 

pairs out of the total 4,815 for which only fare data is available. With better, more homogenous data 

sources, this regression model would be able to predict with increased confidence than what is possible at 

present.

5. Results 

5.1. Results from ACES Experiments (N90 and SCT, On-demand/GA and Commercial) 

5.1.1. Introduction 

In this section we present a series of ACES experiments to test the hypothesis that metroplex-bound 

flights will either have a neutral or beneficial effect on overall system performance. Certainly, if they 

have a detrimental effect on performance, then the likelihood of their adoption is unlikely. 

In conducting these experiments, which occurred early in the project’s timeline, the modelers assumed a 

random selection of flights as metroplex-bound, and for each metroplex-bound flight selected, the 

modelers chose the destination airport at random. No attempt was made, in these experiments, to optimize 

the choice of destination airport to minimize aircraft delay or to optimize system performance. The reason 

these experiments were conducted was that the project needed a “quick look” to determine metroplexbound flight performance. Because random variables are used in these experiments, the experiments were 

repeated a number of times to generate a sample from an underlying population, and then the 

“bootstrapping” method, explained later, is used to generate confidence intervals for the actual location of 

the mean performance parameter for each case. 

The metric used is the average delay at each of the target airports. Some of the experiments considered 

N90 metroplex only, while others considered both N90 and SCT. Some of the experiments targeted the 

“on demand” and general aviation market exclusively, while other experiments targeted only commercial 

users. The overall goal of these experiments is to gain a quick look into the performance of the system 

when the metroplex-bound flight option is used by different markets, different user classes, and at 

different percentages of total operations. 

The design of these experiments allow analysis of the entire performance sweep of the concept for 

different flight operators for both N90 and SCT metroplex Bootstrapping provides the statistical analysis 

of the experiments that allows us to quantify the performance results using metrics such as 95% 

confidence interval, mean, and the delay distribution. 

Bootstrapping (24) is a nonparametric technique for statistical inference. It is a computer-intensive 

method that needs a large dataset to work with. The Bootstrapping approach uses the large dataset as a 

population from which small samples are drawn repeatedly. Unlike other parametric statistical methods, 

Bootstrap estimates the sampling distribution of a statistic empirically without making any assumptions 
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about the population explicitly. To estimate the statistic of interest a small sample (bootstrap sample) 

from the population is drawn with replacement by randomly choosing values n number of times. The 

bootstrap sample then estimates the population and any statistics on it would be applicable to the 

population as well. The main sources of error are: 1) The error introduced by picking a particular sample 

from the population and 2) sampling error from not using all possible bootstrap samples. Higher values of 

n would reduce the latter error. 

5.1.2. Air Taxi and GA Flight Analysis 

Metroplex Airline modeling study suggested Air Taxi airlines are better suited to handle the routine 

disruptions expected in a metroplex operation therefore this analysis focuses on air taxi and GA 

operations while leaving commercial airline flights unchanged. To map these benefits of the concept, the 

first set of ACES experiments focus on Air Taxi and GA flights while leaving commercial flights 

unmodified. Aircraft Situation Display to Industry (ASDI) database provides the baseline Flight Data Set 

that includes the origin, destination, time of departure and waypoints for each flight on 07 November 

2008. Running this unmodified flight data set in ACES provides the baseline delay results. 

Table 14 below breaks down the original flight data set into operations (arrival or departure) and flight 

operators (Commercial, Air Taxi and GA). 

Table 14: Original Flight Data Set 

N90 SCT

Flight

Operation Arrivals Departure Arrivals Departure Total

Commercial 1,549 1,545 1,049 1,043 5,090

Air Taxi 292 288 58 64 696

General

Aviation 159 202 129 162 643

Total 2,000 2,035 1,236 1,269 6,429

Commercial flights dominate the flight operations with 80% flights out of the total 6429 flights. 

The aim of the experiments is twofold – to assess the impact of metroplex operations on Air Taxi and GA 

separately and to investigate the impact of increasing the number of metroplex bound flights on total 

delays. Table 15 below shows the experiment design. 
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Table 15: ACES Experiment Design – GA and Air Taxi 

Flight Operators

Air Taxi GA Both

Case % MP

Flights # Runs % MPFlights # Runs % MPFlights # Runs

1 25 10 25 10 25 10

2 50 10 50 10 50 10

3 75 10 75 10 75 10

4 100 10 100 10 100 10

The first case investigates the total arrival delays with 25% of all arrivals designated as metroplex bound 

for Air Taxi, GA and the combination of the two (“both”) flights. The other cases are similar with 50%, 

75% and 100% of all arrivals designated as metroplex-bound. Metroplex bound flights are identified 

randomly from the original flight data set and each such flight is assigned a random destination airport 

from within the metroplex to which the original destination airport belonged. To capture the effects of 

random assignments each case for each flight operator is run 10 times; including the baseline, this takes 

the total number of ACES runs to be 121 (4*3*10 + 1). 

Figure 39 to Figure 42 below show the results of the four experiment cases. The baseline delay of 4043 

minutes, as measured from the ACES experiment run with the original unmodified flight data set, is 

shown in red while the Air Taxi, GA and “Both” cases are shown in blue and green, respectively. Figure 

39 shows the case with 25% of all arrival flights designated as metroplex-bound. All the ACES runs for in 

this category perform better than the baseline, with the best case reducing delays by 11% and the worst 

case reducing delays by 3% over the baseline run. Figure 40, Figure 41, and Figure 42 show that increase 

in metroplex-bound flights results in increased delays for GA flights, while delays from Air Taxi and 

“Both” flights remain less than the baseline case. 

3500

3550

3600

3650

3700

3750

3800

3850

3900

3950

4000

4050

4100

1 2 3 4 5 6 7 8 9 10

Total Delays(min)

31 Runs

25% Arrivals MetroplexͲbound

Both

AT

GA

Baseline
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Figure 39: Air Taxi and GA results 25% metroplex-bound flights 

Figure 40: Air Taxi and GA results 50% metroplex-bound flights 

3500

3550

3600

3650

3700

3750

3800

3850

3900

3950

4000

4050

4100

4150

1 2 3 4 5 6 7 8 9 10

Total Delays(min)

31 Runs 50% Arrivals MetroplexͲbound

Both

AT

GA

Baseline
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Figure 41: Air Taxi and GA results 75% metroplex-bound flights 

Figure 42: Air Taxi and GA results 100% metroplex-bound flights 

The results from the ACES experiments show that GA, “Both” and Air Taxi cases result in delays of 

4199, 3787 and 3522 minutes respectively. To investigate the underlying causes of differences in delays 

for GA, Air Taxi and “Both” cases a set of runs from the case 4 (100% Arrivals as metroplex-bound 

flights) are chosen. These runs are highlighted in Figure 42. A comparison of the total number of flight 

arrivals for all airports within the N90 and SCT metroplexes is shown in Figure 43. In the case of 

LaGuardia (KLGA), the number of arrivals for GA case is much more than the Air Taxi and “Both”. 

KLGA has 100 more arrivals than Air Taxi and 93 more than “Both” runs and 12 more than the Baseline 

case.

3500

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3600

3650

3700

3750

3800

3850

3900

3950

4000

4050

4100

4150

4200

4250

1 2 3 4 5 6 7 8 9 10

Total Delay (min)

31 Runs

75% Arrivals MetroplexͲbound

Both

AT

GA

Baseline

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4000

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4250

1 2 3 4 5 6 7 8 9 10

Total Delay (min)

31 Runs 100% Arrivals MetroplexͲbound

Both

AT

GA

Baseline
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Figure 43: Flight arrivals for ACES runs highlighted in Figure 42 

Large number of arrivals would lead to increased delays if the airport capacity constraint is exceeded. 

Flights that are scheduled to arrive at an airport when the arrival capacity has been exceeded would 

experience delays. Any increase in the number of such flights would lead to an increase in total arrival 

delays for that airport. Table 16 below shows the hourly Airport Arrival Rates for all the airports within 

N90 and SCT metroplexes.

Table 16: Airport Arrival and Departure Rates being used for ACES experiments3

ACES Default

Airport AAR

KEWR 59

KLGA 43

KJFK 67

KISP 31

KTEB 43

KLAX 86

KBUR 43

KSNA 31

KONT 59

KLGB 31



3 FAA Airport Capacity Enhancement (ACE) Plan 2000','FAA Advisory Circular (AC) 150/5060Ͳ5' and 'The FAA ACE

Plan 2001'

0

100

200

300

400

500

600

700

800

KEWR KLGA KJFK KISP KTEB KLAX KBUR KLGB KONT KSNA

# Flights

Flight Arrivals

Baseline AT GA BOTH
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The AAR value of 43 is lowest among the three most busy N90 airports, others being KJFK and KEWR. 

Table 17 below shows the total number of arrivals at the airports of N90 and SCT metroplexes. The 

analysis above shows that the low AAR at KLGA combined with the high number of arrivals at KLGA is 

responsible for increased total delays for GA operations during these random ACES experiments. 

Table 17: 100% Arrivals metroplex-bound 

# Flight Arrivals

KEWR KLGA KJFK KISP KTEB KLAX KBUR KLGB KONT KSNA

Baseline 601 558 564 47 212 729 119 73 98 187

BOTH 653 477 550 136 166 706 126 66 124 185

AT 634 470 526 110 242 712 127 74 96 197

GA 626 570 577 71 138 729 116 60 130 171

5.1.2.1. Statistical Analysis

Due to the random nature of this set of metroplex experiments it is important to be able to estimate the 

confidence interval for the delay results generated. There are potentially millions of flight combinations 

possible by varying the number and destination of metroplex bound flights. Bootstrapping provides the 

confidence interval estimates from a smaller subset of such flights. As shown in Table 15, there are a total 

of 40 ACES runs for each flight operator. From this population of 40 results, bootstrap uses 251(25) 

random samples. This bootstrap sample then provides other important metrics such as 95% Confidence 

interval bounds, the standard deviations and the standard error. Figure 44 to Figure 46 show the delay 

distribution for the three operator cases. Similar to the trends from the actual data shown in Figure 39 to 

Figure 42, delay distribution shows the GA case to perform worse than AT and “Both” cases. Comparing 

the mean delay values with baseline, Air Taxi case reduces delay by 8% and “Both” by 7%. Mean delay 

for GA case is just 25 minutes less than baseline. 

Figure 44: Arrival delay probability for Air Taxi case 

AT Baseline

0

5

10

15

20

25

30

35

40

45

50

3400 3600 3800 4000 4200 4400

%

Total delays(mins)

AT MP Delay Probability with 95% CI

AirTaxi

TotalSample (N) 251

confidence level% 95

LowerBound (min) 3637

UpperBound (min) 3797

Mean (min) 3717

Std.Deviation (min) 108

Std. Error (min) 34
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Figure 45: Arrival delay probability for GA case 

 

Figure 46: Arrival delay probability for “Both” case 

These ACES runs with their statistic analyses provide an estimate of the performance envelope of the 

metroplex concept when applied to Air Taxi, GA and a combination of the two. The final results show 

that the concept does reduce total delays at airports given that the capacity constraints are not violated. 

However, the underlying assumption that any aircraft can arrive at any airport within a metroplex is 

unrealistic. Within a metroplex the airports differ in terms of demand-to-capacity ratios, which could be a 

significant factor in the assignment of the destination airport for a metroplex-bound flight. The next set of 

ACES runs investigates the metroplex concept while addressing more realistic constraints. 

5.1.3. ACES Smart Case Analysis 

The previous ACES experiments allowed non-commercial flights to arrive at a random airport within the 

metroplex without any regard to the facilities and congestion levels. Under such a scenario, a small 

GA

Baseline

0

5

10

15

20

25

30

35

40

45

50

3400 3600 3800 4000 4200 4400

%

Total Delays(mins)

GA Delay Probability with 95% CI

GA

TotalSample (N) 251

confidence level% 95

LowerBound (min) 3920

UpperBound (min) 4106

Mean (min) 4017

Std.Deviation (min) 134

Std. Error (min) 42

Both Baseline

0

5

10

15

20

25

30

35

40

45

50

3400 3600 3800 4000 4200 4400

%

Total Delay (min/flight)

"Both" Delay Probability with 95% CI

“Both”

TotalSample (N) 251

confidence level% 95

LowerBound (min) 3660

UpperBound (min) 3804

Mean (min) 3733

Std.Deviation (min) 93

Std. Error (min) 30
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passenger aircraft such as a Beechcraft Bonanza originally flying to Teterboro (KTEB) could be assigned 

to land at KJFK. This switch from a small airport to a large, busy international airport is unrealistic. Even 

if there are marginal delays to be saved here, the pilot would likely prefer a small airport with fewer widebody aircraft and less congestion. These “smart” experiments restrict the destination airport assignment to 

airports that are of similar size and have comparable facilities as compared to the original destination 

airport in the baseline flight data set. Previous results point out that N90 airports operate at close to their 

capacity and experienced maximum delays for the unconstrained metroplex operations. These airports 

would therefore serve as excellent test cases for the experiments with more realistic constraints. To 

simulate this “smart” concept, ACES was setup so that flights to big three (KEWR, KJFK, KLGA) 

airports are randomly assigned to one of the big three, while flights to the small three (KTEB, KISP, 

KFRG) are randomly assigned to one of them. These experiments designate all GA and Air Taxi flights as 

metroplex-bound i.e. 100% Air Taxi and GA flights are metroplex bound. The experiment design is 

shown in Table 18 below. The 40 ACES runs provide the initial population for Bootstrap. 

Table 18: ACES Experiment Design – “Smart” case 

Flight Operator

Both

Case % MP

Flights # Runs

1 100 40

Analysis of ten such ACES runs helps in understanding the underlying reasons for differences in delays 

among these experiments. Figure 47 below plots the total arrival delays at N90 airports from 10 ACES 

runs. The results show that all the “smart” case runs result in lower delays than the baseline. Although 

variation in the delays for each run is evident, it is quite small computed as minutes per flight. 

Figure 47: ACES Smart case sample results 

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3950

4000

4050

4100

1 2 3 4 5 6 7 8 9 10

Total Delay (min)

ACES Runs

100% Arrivals MP

Baseline Both 100% "Smart"
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The delays range from 0.56 minutes per flight to 0.61 minutes per flight. It is important to note that these 

delays are results of flight operations at the N90 and SCT metroplex only. It is likely that experiments that 

include all the other flights would result in much higher delays; therefore it is important to identify key 

reasons for these delay variations. In Figure 47, ACES runs 4 and 6 result in the highest and lowest delays 

respectively. The Air Taxi and GA results presented in the previous section identified airport arrival 

capacity constraint as the primary reason for arrival delays; the current analysis starts with the same 

approach. Figure 48 compares the number of flight arrivals and total delays at each N90 airport. 

Figure 48: Arrivals and Delay comparison – ACES run 4 and 6 from Figure 47 

Among the three busiest airports, KLGA and KEWR experience the most variation in number of arrivals 

from baseline, to Run 4 and 6. Figure 49 shows the total Air Taxi and GA arrivals for the baseline case. 

Figure 49: Baseline Air Taxi and GA arrivals at KJFK, KEWR and KLGA airports 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

0

100

200

300

400

500

600

700

800

KJFK KEWR KLGA KISP KTEB KFRG Delay

Total Delays (min)

# Arrivals

Flight Arrivals and Delay

Baseline Run 4 Run 6 Baseline Delay Run 4 Delay Run 6 Delay

0

20

40

60

80

100

120

140

KJFK KEWR KLGA

# Flights

AT and GA ArrivalsͲ Baseline

GA

AT

Both
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KLGA experiences the majority of Air Taxi traffic therefore for any random metroplex assignment, it is 

more likely that Air Taxi flights are distributed away from KLGA. This is true for the ACES runs 4 and 6 

and in both these cases the number of arrivals is less than the baseline case. As the number of arrivals at 

KLGA, which is the first of the big airports to reach arrival capacity, decreases so do the total delays. 

Table 19 shows the arrival delays for runs 4 and 6. 

Table 19: Arrival Delays for ACES runs 4 and 6 from Figure 47 

 Baseline Run 4 Run 6

KLGA 791 456 402

KWER 297 404 469

KJFK 711 905 511

Total 1799 1765 1382

5.1.3.1. Statistical Analysis

A Bootstrap analysis further investigates this behavior. A total of 40 ACES runs for the Smart case 

provide the initial Bootstrap population. Choosing random samples with replacement expands the sample 

size to 251. 95% Confidence Interval bounds, mean, and errors for this sample estimates these statistics 

for the entire population. 

 

Figure 50: Arrival delay probability for “Smart” case 

The bootstrap results show that smart runs for N90 perform better than the baseline. By comparing the 

mean delays with the baseline shows that the realistically constrained “smart” ACES runs reduced the 

total delay by 3%. Due to more stringent constraints, this delay saving of 3% is lower than the 7% and 

8% provided by Air Taxi and “Both” cases. 

Previous experiments deal exclusively with non-commercial flights however commercial flights account 

for 80% of all operations at N90 and SCT metroplexes. The next set of experiments analyzes the effects 

of metroplex operation using commercial flights. 

"Smart" Case

Baseline

0

5

10

15

20

25

30

35

40

45

50

3400 3600 3800 4000 4200 4400

%

Total Delays(min)

MP Delay Probability with 95% CI

“Smart”

TotalSample (N) 251

confidence level% 95

LowerBound (min) 3843

UpperBound (min) 4011

Mean (min) 3928

Std.Deviation (min) 120

Std. Error (min) 38
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5.1.4. Commercial Only Smart Case Analysis 

Since majority of the flying public use commercial flights it is important to investigate the effects of 

metroplex operations on delays for such flights. To identify performance trends by using commercial we 

run Air Taxi and GA flights as they were in the original flight data set. Purdue University’s Flexible 

Flight Selection model identifies N90 bound 478 flights from the original flight data set as metroplex 

bound. It uses historical data to identify flights that are most likely to have fewer than five connecting 

passengers. As with previous experiments we run SCT metroplex flights that were in the original flight 

data set. For each ACES run these 478 flights are assigned a random destination airport from within the 

N90 metroplex. The destination airport assignment follows the realistic constraint that ensures the flight 

arrives at an airport that is similar in size and facilities to the one at which it was originally scheduled to 

arrive. 40 such runs provide the results for analysis. Table 20 shows the experiment design. 

Table 20: ACES Experiment Design – “Commercial Smart” case 

Flight Operator

Commercial

Case % MP

Flights # Runs

1 100 40

Figure 51 shows the delay results from the 40 ACES runs. The delays vary from 3994 (Run_MIN) 

minutes to 4735 (Run_MAX) minutes. Of all the 40 experiments only four produced delays lower than 

the baseline.

Figure 51: ACES Commercial Smart case results 

Table 21 shows the number of arrivals for the baseline, Run_MAX and Run_MIN cases at the three N90 

airports.

Run_MAX

Baseline Run_MIN 3900

4000

4100

4200

4300

4400

4500

4600

4700

4800

1 2 3 4 5 6 7 8 9 10

Total Delay (min)

ACES Runs

N90 Commercial Smart
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Table 21: Flight arrivals at N90 airports for Commercial Smart case 

# Flights Arrivals

KJFK KEWR KLGA Total KJFK + KLGA

Baseline 564 601 558 1723 1122

Run_MAX 683 587 453 1723 1136

Run_MIN 678 631 414 1723 1092

Since this is a smart run where metroplex flights only arrive at airports that are similar to the ones they 

were originally assigned to, all three runs result in the same total flight arrivals for the big airports at N90. 

Comparison of the total number of flights arriving at JFK and LGA shows that the Run_MAX case has 

the maximum number, followed by the baseline case, followed by the Run_MIN case. The number of 

arrivals at KJFK and KLGA fall in the same order as the total delays for these runs. Table 22 shows the 

total delays at KJFK, KLGA and KEWR for the three runs. The Run_MAX case results in the highest 

total delays, followed by the baseline and Run_MIN cases. This further establishes the relationship 

between large number of arrivals and greater total delays at KJFK and KLGA. This is likely to be the 

effect of violation of the arrival capacity constraints at KJFK and KLGA. 

Table 22: Arrival delays at N90 airports for Commercial Smart case 

Arrival Delays (min)

KJFK KEWR KLGA Total KJFK + KLGA

Baseline 712 297 791 1800 1502

Run_MAX 1430 388 446 2264 1876

Run_MIN 909 334 301 1544 1209

The total number of arrivals over capacity at these airports is shown in Table 23. The ‘Run_MAX’ case 

has the maximum number of arrivals over capacity while the ‘Run_MIN’ case has the minimum; this is in 

line with the delay trends of Table 22 proving that the congestion at KJFK and KLGA is the main cause 

of increased delays seen in ACES runs that involve flying commercial flights as metroplex-bound. 

Table 23: Arrivals over capacity at KJFK and KLGA for Commercial Smart case 

Arrivals Over Capacity

KJFK KLGA KJFK + KLGA

Baseline 56 71 127

MAX 110 22 132

MIN 101 14 115
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5.1.5. Commercial only distinct N90 and SCT Analysis 

For all previous ACES experiments, the original flight data set was modified to estimate the effects of 

metroplex operations by assigning certain flights a random destination airport while leaving all other 

flights as they were. These analyses showed that N90 airports experienced higher delays due to their 

congested airspace that exceeded the airports’ capacities. In order to rule out effects of traffic at SCT on 

N90 results and vice-versa, the next ACES experiments use two distinct flight data sets, one each for the 

N90 and the SCT analysis. The flight data set for N90 (FDS_N90) consists of only those flights whose 

origin or destination was an airport in the N90 metroplex. The flight data set for SCT (FDS_SCT) 

contains only flights whose origin or destination was an airport in the SCT metroplex. Table 24 shows the 

number of flights in each flight data set by flight operator. 

Table 24: Flights by operators - Flight Data Sets FDS_90 and FDS_SCT 

Metroplex N90 SCT

FDS FDS_N90 FDS_SCT

Commercial Flights 3093 2091

Air Taxi Flights 578 119

GA Flights 358 288

Total 4029 2498

Table 25 and Table 26 show number of flight arrivals and departures for N90 and SCT metroplexes 

respectively. 

Table 25: Flights by operations - Flight Data Set FDS_N90 

FDS_N90 Flights

Departure Arrivals

KJFK 584 573

KEWR 592 605

KLGA 552 558

KTEB 257 216

KFRG 4 2

KISP 50 48

N90 Total 2039 2002
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 Table 26: Flights by operations – Flight Data Set FDS_SCT 

FDS_SCT Flights

 Departure Arrivals

KLAX 737 740

KBUR 116 123

KSNA 239 195

KONT 111 99

KLGB 66 79

SCT Total 1269 1236

From these flight data sets, the Flexible Flight Selection lists 30% and 50% commercial flights as 

metroplex bound for N90 and SCT metroplexes. Each such list forms one set of ACES experiments as 

shown in Table 27 below. 

Table 27: ACES Experiment Design – Commercial only distinct N90 and SCT case 

Experiments Metroplex %Flexible Flights No. of ACES

Run

1 N90 0 (Baseline) 1

2 N90 30 40

3 N90 50 40

4 SCT 0 (Baseline) 1

5 SCT 30 40

6 SCT 50 40

The first and fourth experiments represent the baseline case for N90 and SCT metroplexes consisting of 

one ACES run each. As with previous experiments, 40 ACES runs provide the population for statistical 

analysis using Bootstrap approach. The second and fifth experiments involve 40 ACES runs with 30% 

commercial flights designated as metroplex-bound. Similarly, third and sixth experiments involve 40 

ACES runs with 50% commercial flights as metroplex-bound. Figure 52 and Figure 53 show the results 

for SCT metroplex with 30% and 50% commercial flights selected as metroplex bound. 
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Figure 52: ACES Commercial only distinct SCT case – 30% flight metroplex bound 

Figure 53: Commercial only distinct SCT case – 50% flight metroplex bound 

The SCT results in Figure 52 show that with 30% commercial flights as metroplex bound, the delays are 

comparable with the baseline run. The delays range from 739 minutes (4.5% reduction over baseline) to 

833 minutes (increase of 8% over baseline). With 50% commercial flights operating as metroplex bound, 

only five runs exceed baseline delays and the rest reporting lower delays. The delays range from 704 

minutes (9% reduction over baseline) to 820 minutes (increase of 6% over baseline). 

Baseline

41 Runs

600

650

700

750

800

850

900

1 2 3 4 5 6 7 8 9 10 11

Totoal Delays(min)

ACES Runs

SCT DelaysͲ 30% Flexible Flights

41 Runs

Baseline

600

650

700

750

800

850

900

1 2 3 4 5 6 7 8 9 10 11

Total Delays(min)

ACES Runs

SCT DelaysͲ 50% Flexible Flights
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Bootstrap uses these 40 ACES runs to estimate the delay probability of a flight operating in the SCT 

metroplex where 30% or 50% commercial flights are metroplex bound. Figure 54 and Figure 55 show the 

boot strap results for distinct SCT cases. 

Figure 54: Arrival delay probability for distinct SCT case with 30% flexible flights 

Figure 55: Arrival delay probability for distinct SCT case with 50% flexible flights 

The bootstrap results show that in case of SCT, an increase in the number of metroplex flights leads to a 

decrease in delays. 

Figure 56 and Figure 57 show the results for N90 metroplex with 30% and 50% commercial flights as 

metroplex bound. A majority of runs result in delays greater than the baseline case, with only three out of 

40 runs performing better. The delays range from 1882 minutes (3% delay reduction over baseline) to 

2416 (increase of 24% over baseline). With 50% commercial flights as metroplex-bound all the ACES 

runs report delays exceeding baseline run. The delays range from 1974 minutes (increase of 2% over 

baseline) to 2480 minutes (increase of 28% over baseline). Bootstrap results for distinct N90 metroplex 

with 30% and 50% are shown in Figure 58 and Figure 59, respectively. 

Baseline

0

5

10

15

20

25

30

35

40

45

50

55

60

65

700715730745760775790805820835850

%

Total Delays(min)

SCTDelay Probability with 95% CI

SCT 30% Flexible Flights

TotalSample (N) 251

confidence level% 95

LowerBound (min) 763

UpperBound (min) 788

Mean (min) 774

Std.Deviation (min) 18

Std. Error (min) 5.75

Baseline

0

5

10

15

20

25

30

35

40

45

50

55

60

65

700715730745760775790805820835850

%

Total Delays(min)

SCTDelay Probabilitywith 95% CI

SCT 50% Flexible Flights

TotalSample (N) 251

confidence level% 95

LowerBound (min) 740

UpperBound (min) 769

Mean (min) 755

Std. Deviation (min) 22

Std. Error (min) 7
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Figure 56: ACES Commercial only distinct N90 case – 30% flight metroplex bound 

Figure 57: ACES Commercial only distinct N90 case – 50% flight metroplex bound 

41 Runs

Baseline

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

1 2 3 4 5 6 7 8 9 10 11

Total Delays(min)

ACES Runs

N90 DelaysͲ 30% Flexible Flights

Baseline

41 Runs

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

1 2 3 4 5 6 7 8 9 10 11

Total Delays(min)

ACES Runs

N90 DelaysͲ 50% Flexible Flights
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Figure 58: Arrival delay probability for distinct N90 case with 30% flexible flights 

Figure 59: Arrival delay probability for distinct N90 case with 50% flexible flights 

N90 metroplex bound flights result in delays higher than the baseline runs. This result agrees with those 

from previous studies – “Smart” case and Commercial only smart case. Results from those studies point 

out the constricted arrival capacities at KJFK and KLGA as the main reason for higher delays at these 

airports. Table 28 shows the arrival delays at each N90 airport. This data would help in estimating the 

effects of arrival capacity and congestion on N90 airport delays. KJFK and KLGA account for 79%, 75% 

and 88% of total delays for N90_Baseline, N90_Min and N90_Max runs. They also account for 56%, 

54% and 59% of all arrivals for N90_Baseline, N90_Min and N90_Max runs. Further analysis would 

show that the number of arrivals could justify the delay trends for N90 airports. 

0

5

10

15

20

25

30

35

40

45

50

55

60

65

1800186019201980204021002160222022802340240024602520

%

Total Delays(min)

N90 Delay Probability with 95% CI

N90 30% Flexible Flights

TotalSample (N) 251

confidence level% 95

LowerBound (min) 2021

UpperBound (min) 2152

Mean (min) 2076

Std.Deviation (min) 103

Std. Error (min) 32

0

5

10

15

20

25

30

35

40

45

50

55

60

65

18001860192019802040210021602220228023402400246025202580

%

Total Delays(min)

N90 Delay Probabilitywith 95% CI

N90 50% Flexible Flights

TotalSample (N) 251

confidence level% 95

LowerBound (min) 2126

UpperBound (min) 2295

Mean (min) 2212

Std.Deviation (min) 133

Std. Error (min) 42
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Table 28: Arrival delay analysis –Distinct N90 case 

N90 Delay Analysis (min)

N90_Baseline N90_Min Delay N90_Max Delay

# Arrivals Arrival Delay # Arrivals Arrival Delay # Arrivals Arrival Delay

KJFK 397 423 490 564 497 1030

KEWR 450 217 465 229 398 157

KLGA 423 577 315 254 375 465

KTEB 158 38 158 35 158 35

KFRG 2 4 2 4 2 4

KISP 34 2 34 2 34 2

N90 Total 1464 1261 1464 1088 1464 1693

Table 29 shows the landing delays at KJFK, KLGA and KEWR due to arrival constraint violations. 

Table 29: AAR landing delay comparisons – Distinct N90 case 

N90_Baseline (min) N90_Min Delay (min) N90_Max Delay (min)

AAR Landing ADR Takeoff AAR Landing ADR Takeoff AAR Landing ADR Takeoff

KJFK 250 305 368 422 635 415

KEWR 165 104 178 104 134 104

KLGA 372 183 171 183 308 183

Comparison between N90_Max and N90_Min runs on AAR Landing delays and number of arrivals 

shows that a difference of seven arrivals results in an increase of 247 minutes of delay. This analysis 

shows that the difference in number of arrivals alone does not lead to proportionate differences in delays. 

Comparison between the two flight schedules would help in identifying the differences between the two 

runs and the time of their occurrence. In Figure 60, flight operation for N90_Max run is shown in red. The 

difference between N90_Min and N90_Max are shown in black. The periods of positive black columns 

coincides with periods of high traffic volume for the N90_Max run. This means that for N90_Max run the 

additional metroplex bound flights arrive at times of peak traffic thus resulting in larger delays even with 

comparable number of total flight arrivals than in the N90_Min run. 
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Figure 60: Flight operation comparison – N90_max and N90_Min runs 

5.2. Results from LTV – McTMA model 

To evaluate the benefit of flexible operations and the integrated model on the metroplex regions, actual 

flight data into N90 Metroplex was utilized to test the integrated LTV-McTMA model. Figure 61 shows 

the stacked ETA distribution of the historical data for three major airports (EWR, JFK, LGA) on N90. An 

interesting demand characteristic is that traffic demand at JFK between 1:00 and 5:00 is much less than 

that at either LGA or EWR. On the other hand, demand at JFK from 16:00 to 23:00 increases steeply and 

overall demand among three airports seems to be evenly distributed compared to those at earlier time 

periods.

Given all ETAs, McTMA determines the required time of arrival (RTA) for each flight at every meter fix 

and airport while considering critical operational constraints. Delay for each flight at meter points and the 

airport is defined as the difference between the unimpeded ETA and RTA. 

Ͳ10

Ͳ5

0

5

10

15

20

25

30

0:00:000:45:001:30:002:15:003:00:003:45:004:30:005:15:006:00:006:45:007:30:008:15:009:00:009:45:00

10:30:0011:15:0012:00:0012:45:0013:30:0014:15:0015:00:0015:45:0016:30:0017:15:0018:00:0018:45:0019:30:0020:15:0021:00:0021:45:0022:30:0023:15:00

1/1/1900 0:00

Traffic Pattern at KJFK for N90 Max Delay

N90 MaxͲ N90 Min N90 Max
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Figure 61: Traffic demand on N90 

50

40353025201510

aircraft of No.

ETA bin (15min)

Trafficdemand on N90

LGA_ETAJFK_ETAEWR_ETA
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Table 30 shows an example of the McTMA scheduling result for the major airports in N90. A total of 

1524 flights on November 7, 2008 were tested and scheduled at 14 arrival fixes and 3 major airports. As 

indicated in the highlighted area, the McTMA scheduler schedules aircraft by assigning the necessary 

delay to keep aircraft separated based on wake vortex separation and proper separation at the meter fixes. 

No additional arrival rates are set in this analysis. 

One possible limitation of this scheduling analysis is to approximate the computation of wake vortex 

separation by using speeds at five minutes before touchdown, because no detailed runway configuration 

exists in the current data. 

Table 30: Scheduling example output 

Table 31 indicates the computed total delays for nominal flights (non-optimized) at the 14 meter fixes and 

3 major airports. JFK incurred about twice as much delay as the other two airports, even when it had the 

smallest number of arriving aircraft. That is possibly because the traffic demand to JFK is not evenly 

distributed over an entire day. Among 14 meter fixes, MUGZY, which is shared by EWR and JFK, had 

the largest delay. 

EWR ETA STA

JBU540 20:03:00 20:04:28

BTA2852 20:08:00 20:08:00

COA1580 20:08:00 20:09:17

COA1177 20:09:00 20:10:34

COA85 20:10:00 20:11:33

AVI1 20:10:00 20:13:13

BTA2864 20:11:00 20:14:33

COA111 20:11:00 20:15:55

COA1134 20:11:00 20:17:12

UCA8640 20:13:00 20:18:29

COA769 20:13:00 20:19:44

DLH402 20:14:00 20:20:40

COA1403 20:14:00 20:22:17

CJC3406 20:15:00 20:23:34

BTA2342 20:16:00 20:24:51

TRS577 20:17:00 20:26:08

CJC3253 20:19:00 20:27:25

BTA2939 20:21:00 20:28:46

COA55 20:21:00 20:29:48

COA171 20:21:00 20:31:28

COA186 20:24:00 20:32:45

COA71 20:24:00 20:33:43

JBU554 20:25:00 20:35:20

BTA2533 20:29:00 20:36:38

JFK ETA STA

FIN5 20:04:00 20:05:25

AFR6 20:09:00 20:09:00

AMX402 20:11:00 20:11:00

SWR14H 20:12:00 20:12:00

JBU1086 20:13:00 20:13:40

EGF4656 20:16:00 20:16:00

DAL99 20:16:00 20:16:57

NWA895 20:17:00 20:18:35

JBU832 20:18:00 20:19:49

EIN111 20:18:00 20:20:44

DAL30 20:20:00 20:22:27

FRL214 20:21:00 20:23:49

UAL6 20:22:00 20:25:04

JBU94 20:23:00 20:26:17

JBU605 20:26:00 20:27:30

EGF4750 20:27:00 20:28:43

COM727 20:29:00 20:29:56

CPA94 20:30:00 20:30:53

LGA ETA STA

AWE2174 20:01:00 20:02:34

AAL1030 20:04:00 20:04:00

PDT4203 20:04:00 20:05:17

EGF4838 20:06:00 20:06:27

DAL1354 20:12:00 20:12:00

EGF4921 20:14:00 20:14:00

DAL1356 20:17:00 20:17:00

AWE2131 20:18:00 20:18:17

NWA508 20:18:00 20:19:33

ACA714 20:24:00 20:24:00

TRS207 20:31:00 20:31:00
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Table 31: Delays for Nominal Flights at 14 meter fixes and 3 airports (Non-optimized) 

5.2.1. Scheduling results 

Before testing the flexible operation concept, we attempted to identify the benefit of just the optimization 

performed by the LTV when there were no flexible flights. ETAs based on the November 7, 2008 traffic 

file were input to McTMA, without optimization, and scheduled. (This traffic file is considered the 

“nominal” traffic file.) The same traffic file then had their ETAs modified by the LTV as discussed above, 

and the resulting traffic file was input to McTMA. (Again, none of these flights were considered flexible.) 

Figure 62 shows the comparison of total delay for nominal flights with that of the LTV-optimized flights. 

Using the LTV optimizer, the total sum of delays at the three airports was reduced to 1,830 minutes (9.5 % 

reduction) as compared to the 2,025 minutes for the nominal traffic file. The LTV optimizer seems to 

condition the flows to reduce arrival peaks at JFK. 

id no. aircraft delay (sec)

SHAFF 0 0

LEMOR 12 928

PENNS 41 1570

Yardley (ARD) 57 1818

VALRE 10 76

NOBBI 6 154

LIZZI 32 352

Robbinsville (RBV) 72 1473

Calverton (CCC) 20 1577

LOVES 2 134

LENDY 0 0

CAMRN 1 0

ZIGGI 1 0

MUGZY 93 3375

KEWR 529 36606

KJFK 484 56916

KLGA 511 27976
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Figure 62: Total delay for 3 airports at N90 for nominal vs. LTV-optimized flights 

More detailed delay statistics for both nominal and LTV-optimize flights are shown in Table 32. Overall, 

the LTV optimizer has a positive effect on delay reduction even though the main function of LTV 

optimizer is not to decrease total delay. 

Table 32: Delay Statistics for 3 airports at N90 for nominal vs. LTV-optimized flights 

Next, different combinations of flexible flights, with and without LTV-optimization are compared. 

Specifically, under conditions with and without LTV-optimization, different percentages of flexible 

flights were compared – 0%, 10%, 20%, 30%, 40%, and 50%. (The 0% cases are discussed above – the 

“nominal” and “optimized” cases whose results are shown in Figure 62: Total delay for 3 airports at N90 

for nominal vs. LTV-optimized flights and Table 32.) 

Figure 63 and Table 33 show how total delay changes depending on the percentage of flexible flights. As 

the percentage of flexible flights increase, the total sum of delay for the three major airports decreases 

almost linearly. This suggests that McTMA and ARB reduce overall delay by reassigning flexible flights 

to less congested slots. Compared to the nominal case, the benefit of delay reduction reaches 29.4% at 50% 

flexible flights. 
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Figure 63: Total delays for various flexible flight percentages into N90 for November 7, 2008 

Table 33: Delay reduction for various flexible flight percentages into N90 for November 7, 2008 

Next, the same percentage of flexible flights was used in conjunction with LTV optimization. The results 

are shown in Figure 64 and Table 34. The results exhibit similar behavior to the non-optimized case, but 

with a somewhat larger benefit in terms of delay reduction. Combining the integrated model with 50% 

flexible flights chosen resulted in a reduction in total delay of 40.8%. 

In Figure 64, it is observed that delay with 20% flexible flights the delay slightly increases over the 10% 

case, which is a deviation from the otherwise-linear decreasing trends of delay reduction. While the 

reason for this is unknown, there is likely some variability in the benefits obtained due to the particular 

traffic file. 

KEWR KJFK KLGA Total Reduction

Nominal (No FF) 610.1 948.6 466.3 2025.0

10% 586.6 1001.4 316.2 1904.2 Ͳ6.0%

20% 547.9 975.3 313.3 1836.5 Ͳ9.3%

30% 414.7 967.0 322.4 1704.2 Ͳ15.8%

40% 377.4 825.5 406.2 1609.1 Ͳ20.5%

50% 389.3 586.4 453.4 1429.1 Ͳ29.4%
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Figure 64: Total delay for various flexible flight percentages into N90 for November 7, 2008 

Table 34: Delay reduction for various flexible flight percentages into N90 for November 7, 2008 

A comparison of the total delay for the four different approaches is shown in Figure 65. Level of flexible 

operations is assigned to be 30%. LTV-optimization makes relatively small saving (about 10%) by 

providing McTMA scheduler with better ETAs. However, we can see that combing LTV optimized flows 

with flexible operations produces much larger reduction of delay. This is because the integrated model 

(LTV+McTMA) with flexible flights off-loads the congested airports (i.e., balances runway operations) 

resulting in reduced delay. 

KEWR KJFK KLGA Total Reduction

Nominal (No FF) 610.1 948.6 466.3 2025.0 0.0%

LTVͲOpt (No FF) 619.0 755.3 456.4 1830.8 Ͳ9.6%

10% 583.0 778.6 279.6 1641.2 Ͳ19.0%

20% 547.1 761.8 349.7 1658.6 Ͳ18.1%

30% 414.5 731.6 353.2 1499.3 Ͳ26.0%

40% 369.2 552.4 321.2 1242.8 Ͳ38.6%

50% 352.2 485.8 360.3 1198.2 Ͳ40.8%
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Figure 65: Total delay comparison for the four approaches for the November 7, 2008 data file 

5.2.2. Cost of delay for N90 

The cost of total delay at N90 for nominal and flexible flights was estimated using cost figures generated 

for NASA and described in a report by Ferguson et al. (2011). Specifically, the “air cost” of delay, 

identified in the report in aggregate form, was adjusted for differences in the cost of delay by airport. The 

results are shown in Table 35, which shows the cost of delay for the nominal traffic file, and in Table 36, 

which shows the cost of delay for LTV-optimized flights with 30% flexible flights. The difference 

between these delays, $22,509, is an estimate of the savings obtained by using 30% flexible flights and 

the LTV optimizer for this metroplex on this day. (There are numerous limitations to this result that 

warrant caution in its interpretation, including the use of only one traffic file, one metroplex, and 

numerous simplifying assumptions such as having all delay taken enroute.) 

Table 35: Estimate of the cost of delay at N90 for the nominal traffic file 
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Table 36: Estimate of the cost of delay for LTV-optimized flights and 30% flexible flights at N90 for 

November 7, 2008 

Table 37 shows the cost savings derived from different percentages of flexible flights into N90. The 

savings are the difference between the cost of delay for nominal flights and the cost of delay for that level 

of flexible flights. Also shown in Table 37 is the amount saved per flight, obtained by dividing the total 

benefit by the number of flexible flights. This is an estimate of the amount the ticket prices could be 

reduced, in aggregate form, if the savings were applied to the flexible flights without profit taken by the 

airlines. (The savings per flight would have to be divided by the number of seats on the flight to obtain 

the average reduction for each fare.) Alternately, this amount could be saved by the airlines on each flight. 

Figure 66 depicts the savings per flight graphically. After 10% flexible flights, the savings stays relatively 

constant.

Table 37: Savings by percentage of flexible flights 
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Figure 66: Savings in dollars per flight by percentage of flexible flights 

5.2.3. Scheduling Results on SCT Metroplex data 

In order to test the proposed integrated model and flexible concept on a different metroplex, SCT flights 

data have been also tested. In the SCT metroplex, 5 major airports (BUR, LAX, LGB, ONT, SNA) and 4 

meter fixes are considered for scheduling purposes. 

The SCT metroplex is dominate by LAX in terms of demand and delay. As shown in Table 38 and Figure 

67, LAX has over 90% of the SCT delay. Since flexible operations reduce arrival peaks by reassigning 

flexible flights into less congested airports, one would expect that the benefits of the concept in SCT 

would be substantially greater than found for N90, where the delay is more balanced across the airports in 

the metroplex.

As shown in Figure 68 and Figure 69, the delay savings is substantially greater than for N90. The delay 

savings exceeds 70% when the LTV-optimizer is used and 50% flexible flights are available. (The ARB 

did not constrain aircraft to landing at compatible airfields and therefore may allow for greater 

rescheduling flexibility than really exists.) 

$Ͳ

$20.00

$40.00

$60.00

$80.00

$100.00

$120.00

10% FF 20% FF 30% FF 40% FF 50% FF

$ per flight
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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Table 38: Demand and delay for SCT traffic file 

Figure 67: Delay distribution for the SCT metroplex airports for the nominal case vs. LTV-optimized case 

with 30% flexible flights 

NonͲopt & No FF LTVͲopt & 30% FF

# aircraft delay (min) # aircraft delay (min)

KBUR 109 15.7 118 19.8

KLAX 577 1309.3 531 722.6

KLGB 50 0.3 84 2.2

KONT 92 13.5 102 14.9

KSNA 154 39.2 147 23.0

Total 982 1377.8 982 782.4

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0

KBUR KLAX KLGB KONT KSNA

delay (min)

5 major airports

Delay on SCT

NonͲopt & No FF

LTVͲopt & 30% FF
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Figure 68: Nominal vs. LTV-optimized/30% flexible flight demand on the airports in the SCT metroplex 

Figure 69: Total delay reduction in SCT with and without LTV-optimization, and at various percentages of 

flexible flights 

0

100

200

300

400

500

600

700

KBUR KLAX KLGB KONT KSNA

# arrivals

5 major airports

# arrivals on SCT

NonͲopt & No FF

LTVͲopt & 30% FF

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0

1600.0

No FF 10% 20% 30% 40% 50%

Total delay (min)

% of flexible flights

Delay for flexible flights

NonͲopt+FF

LTVͲopt+FF

5.5% 25.9% 31.5% 43.2% 60.6% 71.6%
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5.3. Best Case Results from a Simple Flight Allocation 

What is the best case scenario in reducing arrival delays at congested airports? If it is possible to shift any 

flight landing at a congested airport to a less congested airport without any consideration of constraints 

(e.g. vortex separation, proper separation between flights, etc.), arrival delay can be reduced significantly. 

However, it is not realistically possible. One of the major reasons to analyze the best case is to have a 

lower boundary of delay reduction. These results, a form of best case, can be compared to ACES and 

LTV+McTMA results. Figure 70 depicts traffic demands and airport arrival rate (AAR) for N90 airports. 

X axis represents 15 min. time intervals and Y axis represents the number of flights within the time 

interval. The goal of the best case is to minimize the number of flights landing at an over-capacitated 

airport by shifting flights (called flexible flight operations) between airports. In order to calculate total 

arrival delays, flights in the overcapacity interval shift to next time interval, and along with that a 15 min. 

delay for each shifting flight is added to total delays. As expected, there are cons and pros to this analysis. 

Since this method uses a simple flight allocation, costs involved in writing and running computer codes 

are low. But a striking con is that this simple method makes errors of total delays because the smallest 

unit for delay is assumed to be 15 min. 

Figure 70: Traffic demand and AAR (Airport Arrival Rate) for N90 airports on November 7th in 2008 

In this analysis, two cases are run: unrestricted flexible flight allocation (a best case estimate) and 

restricted flexible flight scheduling. In the unrestricted flexible flight allocation case, all flights have a 

possibility to be a flexible flight, but in restricted flexible flight scheduling; only 30% of the flights 

0 500 1000 1500 2000 0

2

4

6

8

10

12

14

16

18

Time (15min)

# of Operations within 15min interval at JFK

0 500 1000 1500 2000 0

2

4

6

8

10

12

14

16

18

Time (15min)

# of Operations within 15min interval at LGA

0 500 1000 1500 2000 0

2

4

6

8

10

12

14

16

18

Time (15min)

# of Operations within 15min interval at EWR

EWR

LGA

AAR = 38 JFK

AAR = 38

AAR = 49
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

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chosen by the FFS model can be considered as flexible flights. Even though a flight is at an overcapacity 

airport, if the flight is not in the candidate flexible flight list, it cannot be moved to another airport. Table 

39 shows the results of a best case estimate and restricted flexible flight scheduling for N90 and SCT. 

These two cases are compared to baseline results from ACES. At N90, total arrival delays of best case 

estimates reduce dramatically compared to baseline case, but total delays from restricted scheduling case 

are much higher than baseline results because this model assumes 15 min. delays, which is an important 

limitation of this model. For SCT, there are no delays because there are no traffic demands over capacity. 

In other words, ample capacity across all SCT airports meant allocation never resulted in any delay. 

Table 39: Arrival delays from a simple flight allocation method for N90 and SCT 

5.4. Comparison of results between all cases 

In this section, we show a brief delay comparison at N90 for ACES’s simulation, integrated LTVMcTMA model with flexible operation concept, and the best-case estimate described in the previous 

section. For the comparison of flexible operation concept, 30% of flexible flights are selected. As 

described in the previous section, if more flexible flights are chosen, more savings of delay reduction 

would be obtained. Table 40 summarizes the ACES runs (min and max savings) as compared to the 

baseline from the ACES simulation. The “All FF allocation” is the best possible savings achievable, using 

essentially manual rescheduling with maximum, perhaps unrealistic, flexibility, although the delay 

incurred comes in chunks of a minimum of 15 minutes. The “restricted FF” scheduling shows the savings 

achievable when only the 30% of the flights designated as flexible could be moved to a different airport. 

The remainder of the rows shows the effect of different scheduling options, again with 30% flexible 

flights, as compared to the McTMA baseline. (The differences in baseline delay reflect very different 

methods for scheduling aircraft between ACES and McTMA.) 

Table 40: Total delay comparison between the ACES simulation, the best case scenario, and the LTVoptimized/30% flexible flight case 

Arrival Delay Minutes Total

Baseline (ACES) 1,940

Random FF (ACES)Ͳmin 1,882

Random FF (ACES)Ͳmax 2,416

All FF allocation (a best

case estimate) 150

Restricted FF scheduling 2,655

McTMA 2,025

LTVͲopt + McTMA 1,830

FFS+McTMA 1,704

FFS+LTVͲopt + McTMA 1,500
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The savings for the ACES runs is, at best, approximately 3%. The savings for McTMA is 26%. The 

substantially greater savings indicates that the scheduler is choosing flights to move much more 

intelligently than the random selection used in ACES. Figure 71 compares these estimates graphically. 

Figure 71: Arrival delay for the ACES simulation, best case estimate, and the LTV-optimized/30% flexible 

flight case 

At SCT, the comparison is shown in Table 41. ACES showed an improvement of 24%, while the LTVoptimized/30% flexible flight case showed an improvement of 43%, again indicating that the scheduler is 

choosing flights to reschedule intelligently. 

Table 41: Total delay comparison between ACES, a best case estimate, and the LTV-optimized/30% flexible 

flight case for SCT 

 Arrival delay (min)

 LAX ONT LGB SNA BUR Total

Baseline (ACES) 195 12 8 78 14 307

Random FF (ACES)Ͳmin 103 13 42 55 20 233

Random FF (ACES)Ͳmax 137 14 71 117 11 350

All FF allocation (a best

case estimate) 0 0 0 0 0 0

Restricted FF scheduling 0 0 0 0 0 0

McTMA 1309.3 13.5 0.3 39.2 15.7 1378

LTVͲopt + McTMA 1242 14.4 2.8 34.9 8.7 1302.8

FFS+McTMA 784.5 6.6 1.1 12.6 15.8 820.6

FFS+LTVͲopt + McTMA 722.6 14.9 2.2 23 19.8 782.5

Ͳ

500

1,000

1,500

2,000

2,500

3,000 Arrivaldelay (min.)

JFK

LGA

EWR

Total
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5.5. Intermodal study results 

Passengers traveling on metroplex bound flights could arrive at any one of the metroplex airports; in such 

a scenario they must be aware of the ground transportation options available at all the airports, to go to the 

final destination in the city or to another airport to board a connecting flight. The loss / benefit analysis in 

terms of time and cost of travel from the airport to the final destination would have a big impact on the 

marketability of metroplex tickets. 

Ideally, metroplex airports should be equidistant from each other and the city center such that it would 

take the same amount of time and money to get from any metroplex airport to a downtown location. Since 

in reality the airport locations are not ideal, it is important to estimate the best and worst case scenarios 

that a passenger may face when opting for a metroplex flight. Returning passengers who have a parked 

car at an airport are likely to opt for a standard commercial flight that guarantees their return to the airport 

of their choice. Metroplex flight customers have the option to take on public transportation or use a cab to 

reach their final destination. 

We analyzed data on trip times for the options that travelers have to move between the major airports in 

both N90 and SCT. Table 42 shows the codes used in the subsequent tables. Table 43 and Table 44 show 

the inter-metroplex travel itinerary for passengers using public transport for the SCT and N90 metroplex 

respectively. Google maps4

 provide the data source for public transport itinerary. The distance is shown in 

miles and the total time “TIME” and total walk time “WALK” are in minutes. Walk time can be a metric 

to compare itineraries on passenger preference; itineraries with high walk time are more likely to be 

avoided. 3:00 A.M. and 9:00 A.M. were chosen as non-rush hour and rush hour times, respectively. 

Table 42: Public transport codes 



4 Google Maps, http:\\www.maps.google.com

Code Description

W Walk

B Bus

LR Light Rail

T Train

S Subway

C Cab / Drive
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Table 43: SCT Inter-Metroplex travel using public transport at non-rush hour and rush hour 

Table 44: N90 Inter-Metroplex travel using public transport at non-rush hour and rush hour 

Table 45 and Table 46 show the travel method and time from airport to the city center for SCT 

(Downtown, LA) and N90 (Times Square, NY) during rush-hour and non-rush hours. 

ORIGIN DISTANCE

(miles) METHOD TIME(min)

WALK

(min) METHOD TIME(min)

WALK

(min)

DISTANCE

(miles) METHOD TIME(min)

WALK

(min) METHOD TIME(min)

WALK

(min)

SNA 42.1 WͲBͲBͲBͲBͲW 221 45 WͲBͲBͲBͲBͲW 265 27

LAX 41.2 WͲBͲLRͲBͲBͲBͲW 206 19 WͲBͲLRͲBͲBͲBͲBͲW 228 19

ONT 42.6 WͲBͲBͲBͲTͲBͲW 259 8 WͲBͲTͲTͲBͲW 319 36 57.1 WͲBͲTͲSͲLRͲBͲW 223 30 WͲBͲTͲSͲBͲW 223 45

BUR 52.5 WͲBͲSͲTͲBͲBͲW 227 5 WͲBͲSͲTͲBͲW 237 22 29.4 BͲBͲW 124 23 BͲBͲBͲW 131 24

LGB 27.8 WͲBͲBͲBͲW 141 27 WͲBͲBͲBͲW 167 27 26.2 WͲBͲLRͲLRͲBͲW 132 49 WͲBͲBͲLRͲBͲW 134 27

ORIGIN DISTANCE

(miles) METHOD TIME

(min)

WALK

(min) METHOD TIME

(min)

WALK

(min)

DISTANCE

(miles) METHOD TIME(min)

WALK

(min) METHOD TIME(min)

WALK

(min)

SNA 45.6 WͲBͲTͲTͲTͲBͲBͲW 249 22 WͲBͲBͲBͲBͲTͲBͲW 300 22 52.7 WͲBͲTͲW 205 26 WͲBͲTͲTͲW 210 30

LAX 56.6 WͲBͲBͲTͲBͲW 222 43 WͲBͲBͲTͲBͲW 212 33 32 WͲBͲBͲW 120 16 WͲBͲBͲW 153 26

ONT 51.9 WͲBͲTͲTͲW 151 16 WͲBͲTͲTͲBͲW 180 20

BUR 48.5 BͲTͲTͲBͲW 172 18 BͲTͲTͲBͲW 172 18

LGB 50.6 WͲBͲLRͲSͲTͲBͲW 272 10 WͲBͲLRͲSͲTͲBͲBͲW 216 22 39.4 WͲBͲLRͲSͲTͲW 149 35 WͲBͲBͲLRͲSͲBͲW 154 4

ORIGIN DISTANCE

(miles) METHOD TIME

(min)

WALK

(min) METHOD TIME

(min)

WALK

(min)

SNA 22.6 WͲBͲBͲBͲBͲW 156 24 WͲBͲBͲBͲBͲW 155 7

LAX 22.4 WͲBͲLRͲBͲBͲW 90 18 WͲBͲLRͲLRͲBͲBͲW 101 18

ONT 53.3 WͲBͲTͲSͲLRͲBͲBͲW 204 10 WͲBͲTͲBͲBͲBͲW 238 22

BUR 39 WͲBͲSͲLRͲBͲBͲW 132 4 WͲBͲTͲSͲLRͲBͲBͲW 154 4

LGB

9:00 AM

BUR

3:00 AM 9:00 AM

DESTINATION

LGB

3:00 AM 9:00 AM

SNA

3:00 AM 9:00 AM

LAX

3:00 AM 9:00 AM

ONT

3:00 AM

ORIGIN DISTANCE

(miles) METHOD TIME

(min)

WALK

(min) METHOD TIME

(min)

WALK

(min)

DISTANCE

(miles) METHOD TIME

(min)

WALK

(min) METHOD TIME

(min)

WALK

(min)

EWR 36.3 WͲBͲTͲSͲLRW 126 8 WͲBͲSͲTͲTͲLRͲW 97 8

JFK 30 WͲLRͲTͲTͲBͲW 105 8 WͲLRͲTͲTͲBͲW 101 8

LGA 22.7 WͲBͲSͲTͲBͲW 134 5 BͲSͲTͲTͲBͲW 94 1 12.3 BͲSͲLRͲW 79 7 BͲSͲTͲLRͲW 74 7

ISP 65.2 CͲTͲTͲBͲW 150 1 CͲTͲTͲBͲW 146 1 44.2 CͲTͲRͲW 96 7 CͲTͲRͲW 96 7

ORIGIN DISTANCE

(miles) METHOD TIME(min)

WALK

(min) METHOD TIME

(min)

WALK

(min)

DISTANCE

(miles) METHOD TIME

(min)

WALK

(min) METHOD TIME

(min)

WALK

(min)

EWR 24.2 WͲBͲTͲSͲBͲW 132 9 WͲBͲBͲSͲB 109 1 WͲBͲTͲTͲTͲC 192 1 WͲBͲSͲTͲTͲTͲC 157 1

JFK 12.2 WͲLRͲTͲSͲB 87 7 WͲLRͲSͲSͲB 82 7 WͲRͲTͲC 97 1 WͲRͲTͲTͲC 104 7

LGA BͲSͲTͲC 139 0 BͲSͲTͲTͲC 129 0

ISP 45.7 CͲTͲSͲB 132 0 CͲTͲBͲB 127 0

DESTINATION

EWR JFK

3:00 AM 9:00 AM

LGA

3:00 AM 9:00 AM

ISP

3:00 AM 9:00 AM 3:00 AM 9:00 AM
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Table 45: Public transport travel time estimates to Downtown, LA 

Table 46: Public transport travel time estimates to Times Square, NY 

Public transport may be the most cost effective option available but the frequent changes from bus to train 

to subway makes it less comfortable than using a cab. But travel by taxi is subject to road congestion. 

Texas Transportation Institute’s Urban Mobility Report (26) predicts the general congestion on highways 

and urban areas around the country. The analysis uses data on travel speed and incident-related travel 

delays. The report presents the results in the form of Travel Time Index (TTI) which is the ratio of travel 

time in peak period to travel time at free-flow conditions. As an example, TTI of 1.35 indicates a 20 

minute free-flow trip would take 27 (20 x 1.35) minutes at rush hour. The Texas Transportation Institute 

defines rush hour as 6 A.M. to 9 A.M. and 4 P.M. to 7 P.M. Table 47 presents TTI values for some of the 

most congested urban areas in the country. 

Table 47: Texas Transportation Institute’s Nation Congestion Tables 

ORIGIN DISTANCE

(miles) METHOD TIME(min)

WALK

(min) METHOD TIME(min)

WALK

(min)

SNA 40.7 WͲBͲWͲTͲWͲSͲW 121 29 WͲBͲWͲTͲWͲB 181 55

LAX 18.8 WͲB 72 15 WͲBͲWͲLRͲWͲSͲW 80 28

ONT 40.4 WͲBͲWͲBͲW 145 16 WͲBͲWͲBͲW 135 16

BUR 17.5 WͲBͲWͲSͲW 95 52 WͲBͲWͲSͲW 70 14

LGB 25 WͲBͲWͲSͲWͲSͲW 105 42 WͲBͲWͲSͲWͲSͲW 98 30

SCTͲDowntown, LA

3:00 AM 9:00 AM

ORIGIN DISTANCE

(miles) METHOD TIME(min)

WALK

(min) METHOD TIME(min)

WALK

(min)

EWR 16.9 WͲBͲWͲTͲWͲSͲW 84 14 LRͲWͲTͲWͲSͲW 54 12

JFK 17.8 LRͲWͲSͲW 66 11 LRͲWͲSTͲWͲSͲW 63 15

LGA 9.5 WͲBͲWͲSͲW 58 3 WͲBͲWͲSͲW 56 13

ISP 51.6 CͲTͲWͲSͲW 112 12 CͲTͲWͲSͲW 95 12

N90ͲTime Square, NY

3:00 AM 9:00 AM

SCT

N90
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Using TTI with non-rush hour travel time estimates from Google Maps gives the travel time estimates 

during rush hour. Table 48 and Table 49 below show the travel time estimates for SCT and N90 

metroplexes respectively. 

Table 48: SCT Inter Metroplex travel time estimates using a cab at non-rush hour and rush hour 

Table 49: N90 Inter Metroplex travel time estimates using a cab at non-rush hour and rush hour 

Based on travel time and comfort alone, passengers would choose cab over public transport for almost 

every trip. To assess the impact of costs of travel the next tables show the estimated cab fare for trips to 

city center from the metroplex airports in dollars. The fare calculations are based on government 

guidelines (27) and include the effects of extra time spent in the cab due to traffic. 

ORIGIN SNA LAX ONT BUR LGB

SNA 48 51 63 28

LAX 45 62 39 27

ONT 48 61 59 57

BUR 57 39 60 46

LGB 28 26 56 44

ORIGIN SNA LAX ONT BUR LGB

SNA 72 76 94 42

LAX 67 92 58 40

ONT 72 91 88 85

BUR 85 58 89 69

LGB 42 39 83 66

Non Rush Hour – Google Maps, Mins

TTI Rush Hour, Mins

SCT

ORIGIN LGA EWR JFK ISP Times Sq

LGA 37 16 62 17

EWR 39 46 92 27

JFK 20 47 60 28

ISP 58 87 53 64

ORIGIN LGA EWR JFK ISP Times Sq

LGA 51 22 85 23

EWR 53 63 126 37

JFK 27 64 82 38

ISP 79 119 73 88

Travel Time (mins) Non Rush Hour(3:00 A.M)

DESTINATION

N90

Travel Time (mins) Rush Hour (9:00 A.M)
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Table 50: SCT cab fares to metroplex airports and Downtown, LA 

Table 51: N90 cab fares to metroplex airports and Times Square, NY 

Cost of travel is an important consideration for passengers opting for metroplex flights. For these 

passengers the cost of surface transport would be as important as the airline ticket price. For this reason, 

any cost comparison between a traditional commercial flight and a metroplex flight must include the cost 

of ground transport as well. Table 52 compares the total costs of a business and coach passenger to get 

from Boston to Times Square, NY using public transport and taxi. The table lists two options (taxi and 

public transport) for coach and business passengers. The total costs are sum of the ticket price, and the 

taxi fare or public transport costs5

.



5 Based on recommended Hourly Values of Intercity Surface Mode Travel Time Savings by Department of

Transportations

ORIGIN SNA LAX ONT BUR LGB Downtown

SNA 108 118 136 59 104

LAX 107 144 86 61 51

ONT 116 146 134 136 104

BUR 136 89 135 98 45

LGB 59 60 120 98 66

ORIGIN SNA LAX ONT BUR LGB Downtown

SNA 119 129 149 65 115

LAX 116 158 94 67 56

ONT 126 159 147 148 115

BUR 148 97 148 107 51

LGB 65 65 132 107 73

SCT

FARE ($) Rush Hour (9:00 A.M)

FARE ($) Non Rush Hour (3:00 A.M)

ORIGIN LGA EWR JFK ISP Times Sq.

LGA 55 30 98 25

EWR 56 80 142 41

JFK 31 89 101 45

ISP 97 138 104 110

ORIGIN LGA EWR JFK ISP Times Sq.

LGA 60 32 107 28

EWR 62 87 156 45

JFK 34 96 110 45

ISP 106 151 111 120

FARE ($) Non Rush Hour (3:00 A.M)

FARE ($) Rush Hour (9:00 A.M)

N90
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Table 52: BOS-N90 travel cost comparisons 

The coach fares are the average fares paid by passengers in 2009 Q1. Rush hour cab fares are from table 

19. To target Coach and Business PAX, metroplex airline would need to aim at ticket prices of $100 and 

$113 respectively. 

6. Conclusions 

The primary objectives of this project were to articulate, model, and evaluate a concept for flexible 

operations at a metroplex as a means to optimize the use of metroplex resources. In particular, the 

quantification of best-case benefits from the concept was pursued in order to establish whether further 

research is warranted. A flexible flight is one whose destination airport is not assigned until a threshold is 

reached near the arrival area at which time the runway which reduces overall delay is assigned to that 

flight. The concept seeks to make best use of available metroplex resources via this flexibility.

In summary, the modeling objectives have been met and the resulting quantitative evaluation indicate that 

indeed the concept has potential for significant reductions in delay (and cost due to delay) in the N90 

(New York/New Jersey) and SCT (Southern California) metroplexes. Delay reductions on the order of 26% 

are possible in N90 when 30% of the commercial airline flights (smartly selected by their low probability 

of having many connecting passengers), and nearly 41% when 50% of the flights are flexible. In the SCT 

metroplex, the delay reductions are estimated to be even greater (43% with 30% flexible flights, 71% with 

50% flexible flights). The higher reductions at SCT are due to the fact that this metroplex is less 

constrained currently than N90; thus, there is “more room” to take advantage of flexibility. These findings, 

using an integrated model consisting of enroute optimization, terminal area scheduling and runway 

balancing, were bounded by sets of simulation runs of the concept in ACES under several scenarios. In 

particular, ACES was used to examine the potential impact of using the flexible operations concept for 

on-demand/air taxi and General Aviation (GA) flights; the benefits in NYNJ were especially attractive in 

these scenarios, indicating the flexible operations concepts may be useful to a wide variety of users.

Finally, a set of supporting analyses that examined economic considerations of the concept were 

presented. In particular, a preliminary intermodal analysis illustrated how the flexible operations concept, 

when integrated with information systems that make clear the ground mode options, could be attractive to 

travelers in reaching their destination. These aspects represent a fruitful area of research to more fully 

flesh out the potential workings of the concept.

Ticket Class

Transport by Coach Business

Taxi

Destination

Airport

Coach FARE

($)

Taxi Fare to

Times Sq ($)

Total Cost

($)

Destination

Airport

Business

FARE 1($)

Taxi Fare to Times

Sq ($) Total Cost ($)

LGA 174 28 202 LGA 259 28 287

EWR 183 45 228 EWR 241 45 286

JFK 100 45 145 JFK 148 45 193

Public

Transport

Destination

Airport

Coach FARE

($)

Public Transport

Fare to

Times Sq2 ($)

Total Cost

($)

Destination

Airport

Business

FARE1 ($)

Public Transport

Fare to Times Sq2

($)

Total Cost ($)

LGA 174 14 188 LGA 259 20 279

EWR 183 13 196 EWR 241 19 260

JFK 100 16 116 JFK 148 22 170
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7. One Potential Future Work Item 

While numerous areas of future work were highlighted in the body of this report, one area in particular 

that could leverage recent NASA sponsored research is the use of metroplex dependency functions in 

runway scheduling. Currently, McTMA does not consider internal dependencies in a metroplex before 

allocating flights to runways. It is of utmost importance to consider such dependencies in a metroplex if 

minimizing congestion or increasing throughput is the prime objective. Metroplex dependencies occur 

across different dimensions between the constituent airports. A few example attributes of these 

dimensions are airport proximities, runway configurations, airport operations etc. Purdue, over the past 

three years, have developed a metroplex runway dependency metric based on data collected from sources 

such as Aviation Systems Performance Metrics (ASPM) and Performance Data Analysis and Reporting 

System (PDARS). The purpose of the metric is to point out if the use of a particular runway or a 

particular combination of runways increase or decrease the runway dependencies at a metroplex. As 

dependencies increase, the throughput and thereby the capacity of the metroplex decreases (2). It is 

therefore essential to choose the right combination of runways from available ones at a given time before 

allocating flights. 

A capacity diagram of a metroplex is constructed in order to determine the observed capacity at a 

metroplex for a specific period. This diagram was partially used to validate the metric. In addition to that, 

information from the capacity diagrams is used to develop a Capacity Coverage Chart (CCC). A CCC 

helps to identify those runway configurations at a metroplex that have the highest the capacities and also 

those configurations that are used most frequently at a metroplex. The CCC can therefore be used to 

choose the best configurations. Figure 72 shows the CCC of NYNJ for 2007. 

Figure 72: Capacity coverage chart of NYNJ in 2007. Note that only configurations that are used at least 1% 

of the time in the given year is marked in the figure 

0 10 20 30 40 50 60 70 80 90 100 0

50

100

150

200

250

Percent of time

Hourly capacity (departures+arrivals)

Capacity Coverage Chart: NYNJ

1 2

3 4 5 6 7 8 9 10 11 12 13

14

15

READ: JFK, EWR, LGA

1 — 31L 31R | 31L, 11 22L | 22R, 31 | 4

2 — 31L 31R | 31L, 22L | 22R, 31 | 4

3 — 22L | 22R 31L, 22L | 22R, 13 22 | 13

4 — 31L 31R | 31L, 22L | 22R, 22 | 31

5 — 31L 31R | 31L, 11 22L | 22R, 22 | 31

6 — 4R | 4L 31L, 4R | 4L, 4 | 13

7 — 31L 31R | 31L, 4R | 4L, 31 | 4

8 — 4R | 4L 31L, 22L | 22R, 4 | 13

9 — 22L | 22R 31L, 22L | 22R, 22 | 31

10 — 4R | 4L, 4R | 4L, 4 | 13

11 — 31L 31R | 31L, 22L | 22R, 31 | 31

12 — 31L 31R | 31L, 4R 11 | 4L, 31 | 4

13 — 4L 4R | 4L 31L, 4R | 4L, 4 | 13

14 — 4L 4R | 4L, 4R | 4L, 4 | 13

15 — 22L | 22R, 22L | 22R, AIRPORT CLOSED | AIRPORT CLOSED

4L 4R

JFK

4L 4R

EWR

LGA

13

22

Most used: Configuration 7 (2.6% of the time)

JFK: 31L 31R | 31L

EWR: 4R | 4L LGA: 31 | 4
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

92

The CCC shows the most used metroplex runway configuration and the configuration that produces the 

maximum capacity can be estimated. Based on the runway dependency metric and the capacity coverage 

chart, a technique is proposed (Figure 73) for optimizing runway dependency so that operations can be 

allocated optimally across available metroplex runways. This practice would be beneficial to the flexible 

operations concept as it allows flights to be re-arranged, and thereby potentially creating opportunities to 

minimize runway dependencies. 

Figure 73: Runway dependency optimization process

Operations

Arrivals (Flexible

+Traditional) and

Departures

Check for available

runway options

across the MP

Allocate expected

demand optimally

across active

runways

ǻt
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

93

8. References and Citations 

1. Federal Aviation Administration, “FAA Aerospace Forecasts FY 2008-2025,” 

http://www.faa.gov/data_research/aviation/aerospace_forecasts/2008-2025/ [retrieved 20 December 2010]. 

2. Ayyalasomayajula, S. and DeLaurentis, D., “Developing Strategies for Improved Management of Airport 

Metroplex Resouces,” 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO) and 

Aircraft Noise and Emissions Reduction Symposium (ANERS), Hilton Head, SC, AIAA Paper 2009-7043, Sep. 

2009. 

3. Donaldson, A. and Hansman, R., “Capacity Improvement Potential for the New York Metroplex System,” 10th 

AIAA Aviation Technology, Integration and Operations (ATIO) Conference and AIAA/ISSMO 

Multidisciplinary Analysis Optimization (MAO), Fort Worth, TX, AIAA Paper 2010-9218, Sep. 2010. 

4. Ganesan, R. and Sherry, L., “Adaptive Control of Multistage Airport Departure Planning Process using 

Approximate Dynamic Programming,” [online resource] http://catsr.ite.gmu.edu/pubs/GanesanIIEQC.pdf 

[retreived 20 December 2010]. 

5. Clarke, J.-P. et al., “Characterization of and Concepts for Metroplex Operations,” NASA/CR-2011-216414, 

http://www.aviationsystemsdivision.arc.nasa.gov/publications/2011/NASA-CR-2011-216414.pdf [retrieved July 

2011]. 

6. Expedia Inc., http://www.expedia.com/Home.htm [retrieved August 2010]. 

7. Sun, D., Sridhar, B., and Grabbe, S., “Disaggregation Method for an Aggregate Traffic Flow Management 

Model,” AIAA Journal of Guidance, Control and Dynamics, Vol. 33, No. 3, pp. 666–676. 

8. Landry, S. J., “The Design of a Distributed Scheduling System for Multi-center Time-based Metering of Air 

Traffic into Congested Resources,” Air Traffic Control Quarterly, Vol. 16, No. 1, pp. 69–97. 

9. Meyn, L. et al., “Build 4 of the Airspace Concept Evaluation System,” AIAA Modeling and Simulation 

Technologies Conference and Exhibit, Keystone,CO, AIAA Paper 2006-6110, Aug. 2006. 

10. Bureau of Transportation Statistics, “Data Directory: List of Databases,” [online database], 

http://www.transtats.bts.gov/DataIndex.asp [retrieved 21 July 2010]. 

11. Air Transportation Association, “Annual and Per-Minute Cost of Delays to U.S. Airlines,” 

http://www.airlines.org/Economics/DataAnalysis/Pages/CostofDelays.aspx [retrieved 7 July 2010]. 

12. Castelli, L., Ukovich, W., and Pesenti, R., “An Airline-based Mutilevel Analysis of Airfare Elasticity for 

Passenger Demand,” Air Transport Research Society World Conference, Toulouse, France, Jul. 2003. 

13. Clake, M. D. D., “Irregular Airline Operations: A Review of the State-of-the Practice in Airline Operation 

Control Centers,” Journal of Air Transport Management, Vol. 4, No. 2, pp. 67–76. 

14. Weide, O., Ryan, D., and Ehrgott, M., “Iterative Airline Scheduling,” Technical Report 645, Department of 

Engineering Science, The University of Auckland, New Zealand, 2007. 

15. AhmadBeygi, S., Cohn, A., and Lapp, M., “Decreasing Airline Delay Propagation By Re-Allocating Scheduled 

Slack,” Technical report, AGIFORS, http://www.agifors.org/award/submissions2008/Ahmadbeygi_paper.pdf 

[retireved March 2009]. 

16. Rosenberger, J. M., Johnson, E. L., and Nemhauser, G. L., “A Robust Fleet-Assignment Model with Hub 

Isolation and Short Cycles,” Transportation Science, Vol. 38, No. 3, pp. 357–368. 

17. Lan, S., Barnhart, C., and Clarke, J.-P., “New Approaches to Add Robustness into Airline Schedules,” MIT, 

Course Lecture Notes, 2002. 

18. Lan, S., Clarke, J.-P., and Barnhart, C., “Planning for Robust Airline Operations: Optimizing Aircraft Routings 

and Flight Departure Times to Minimize Passenger Disruptions,” Transportation Science, Vol. 40, No. 1, pp. 15–

28. 

19. Ageeva, Y., and Clarke, J.-P., “Approaches to Incorporating Robustness into Airline Scheduling,” Report No. 

ICAT-2000-8, MIT International Center for Air Transportation, Massachusetts Institue of Technology, 

Cambridge, MA, Aug. 2000. 

20. Personal Communication, Bruce Holmes. [Cited: 2 15, 2011.]. 

21. Cordeau, J.-F., Laporte, G., Potvin, J.-Y., and Savelsbergh, M. W. P., “Transportation on Demand,” Handbook

in Operations Research & Management Science, Vol. 14, pp. 429–466. 

22. Espinoza, D., Garcia, R., Goycoolea, M., Nemhauser, G. L., and Savelsbergh, M. W. P., “Per-Seat, On-Demand 

Air Transportation Part I: Problem Description and an Integer Multi-Commodity Flow Model,” Transportation 

Science, Vol. 42. No. 3, pp. 263–278. 
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

94

23. Rama-Murthy, K., “Modeling of United States Airlines Fares Using Official Airline Guide (OAG) and Airline 

Origin and Destination Survey (DB1B),” Masters Thesis, Department of Civil Engineering, Virginia Polytechnic 

Institute and State University, Blacksburg, VA, Jun. 2006. 

24. Gowrisankaran, G., “Competition and Regulation in the Airline Industry,” FRBSF Economic Letter, No. 2002-

01, Jan. 2002. 

25. Federal Aviation Administration, “Airport Categories,” 

http://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/categories/ [retrieved 20 September 

2010]. 

26. Fox, J., and Weisburg, S., “An R and S-PLUS Companion to Applied Regression: A Web Appendix to the 

Book,” Sage Publications, Thousand Oaks, CA, 2002. 

27. Haukoos, J., and Lewis, R., “Advanced Statistics: Bootstrapping Confidence Intervals for Statistics with 

“Difficult” Distributions,” Academic Emergency Medecine, Vol. 12, No. 4, pp. 360–365. 

28. Schrank, D., and Lomax, T., “2009 Urban Mobility Report,” Report Prepared by the Texas Trasportation 

Institute for University Transportation Center for Mobility, College Station, TX, 2009. 

29. New York City & Limousine Commision, “Passenger Information: Rate of Fare,” 

http://www.nyc.gov/html/tlc/html/passenger/taxicab_rate.shtml [retrieved 9 September 2010]. 

30. Yen, J. W., and Birge, J. R., “A Stochastic Programming Approach to the Airline Crew Scheduling Problem,” 

Transportation Sciences, Vol. 40, No. 1, 3–14. 

31. Sridhar, B., Soni, T., Sheth, K., and Chatterji, G. B., “An Aggregate Flow Model for Air-Traffic Management,” 

Journal of Guidance, Control and Dynamics, Vol. 29, No. 4, pp. 992–997. 

32. Muller E. R., and Chatterji, G. B., “Analysis of Aircraft Arrival and Departure Delay Characteristics,” AIAA's 

Aircraft Technology, Integration, and Operations (ATIO) 2002 Technical, Los Angeles, CA, AIAA Paper 2002-

5866, Oct. 2002. 

33. Geslin, C., “Pricing and Competition in US Airline Markets: Changes in Air Travel Demand Since 2000,” 

Masters Thesis, Dept. of Civil and Environmental Engineering, Massachusetts Institute of Technology, 

Cambridge, MA, 2006. 

34. Kara, A. Q., Ferguson, J., Hoffman, K., and Sherry, L., “Estimating Domestic U.S. Airline Cost of Delay Based 

on European Model,” Proceedings of the 4th International Conference on Research in Air Transportation - 

ICRAT 2010, Budapest, Hungary, Jun. 2010. 

35. Dunbar, M., Froyland, G., and Wu, C.-L., “Robust Airline Schedule Planning: Minimizing Propagated Delay in 

an Integrated Routing and Crewing Framework,” Report, University of New South Wales, Sydney, NSW, 

Australia, http://www.optimization-online.org/DB_HTML/2010/03/2578.html [retrieved August 2010]. 

36. Bhadra, D., Gentry, J., Hogan, B., and Wells, M., “CAASD’s Future Air Traffic Timetable Estimator: A MicroEconometric Approach,” MITRE Technical Papers, 

http://www.mitre.org/work/tech_papers/tech_papers_03/bhadra_estimator/index.html [retrieved March 2010]. 

37. Siek, J. G., Lee. L.-Q., and Lumsdaine, A., “The Boost Graph Library: The User Guide and Reference Manual,” 

Addison-Wesley Professional, Upper Saddle River, NJ, Dec. 2001. 

38. Ganesan, R. and Sherry, L., “Adaptive Control of Multistage Airport Departure Planning Process using 

Approximate Dynamic Programming,” [online resource] http://catsr.ite.gmu.edu/pubs/GanesanIIEQC.pdf 

[retreived 20 December 2010]. 
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

95

9. Appendix A 

DB1B Coupon Columns6:

Column Description Notes 

ItinID Itinerary ID Foreign key to DB1BTicket 

MktID Market ID Foreign key to DB1BMarket

SeqNum Coupon Sequence Number Itinerary-level 

Coupons Number of Coupons in the Itinerary Also in DB1BTicket 

Year Year Also in DB1BTicket 

Quarter Quarter (1-4) Also in DB1BTicket 

Origin Origin Airport Code IATA Airport Code 

OriginAptInd Origin Airport, Multiple Airports Indicator - 

OriginCityNum Origin Airport, City Code - 

OriginCountry Origin Airport, Country Code - 

OriginStateFips Origin Airport, State FIPS Code - 

OriginState Origin Airport, State Code - 

OriginStateName Origin State Name - 

OriginWac Origin Airport, World Area Code - 

Dest Destination Airport Code IATA Airport Code 

DestAptInd Destination Airport, Multiple Airports Indicator - 

DestCityNum Destination Airport, City Code - 

DestCountry Destination Airport, Country Code - 

DestStateFips Destination Airport, State FIPS Code - 

DestState Destination Airport, State Code - 

DestStateName Destination State Name - 

DestWac Destination Airport, World Area Code - 

Break Trip Break Code - 

CouponType Coupon Type Code - 

TkCarrier Ticketing Carrier Code - 

OpCarrier Operating Carrier Code - 

RPCarrier Reporting Carrier Code - 

Passengers Number of Passengers - 

FareClass Fare Class Code - 

Distance Coupon Distance - 

DistanceGroup Distance Group, in 500 Mile Intervals - 

Gateway Gateway Indicator (1=Yes) - 

ItinGeoType Itinerary Geography Type - 

CouponGeoType Coupon Geography Type - 

 



6 http://www.transtats.bts.gov/Fields.asp?Table_ID=289.
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

96

DB1B Market Columns7:

Number Column Description Notes 

1 ItinID Itinerary ID Foreign key to 

DB1BTicket 

2 MktID Market ID Primary key 

3 MktCoupons Number of Coupons in the Market - 

4 Year Year - 

5 Quarter Quarter (1-4) - 

6 Origin Origin Airport Code - 

7 OriginAptInd Origin Airport, Multiple Airports Indicator - 

8 OriginCityNum Origin Airport, City Code - 

9 OriginCountry Origin Airport, Country Code - 

10 OriginStateFips Origin Airport, State FIPS Code - 

11 OriginState Origin Airport, State Code - 

12 OriginStateName Origin State Name - 

13 OriginWac Origin Airport, World Area Code - 

14 Dest Destination Airport Code - 

15 DestAptInd Destination Airport, Multiple Airports 

Indicator -

16 DestCityNum Destination Airport, City Code - 

17 DestCountry Destination Airport, Country Code - 

18 DestStateFips Destination Airport, State FIPS Code - 

19 DestState Destination Airport, State Code - 

20 DestStateName Destination State Name - 

21 DestWac Destination Airport, World Area Code - 

22 AirportGroup Airport Group - 

23 WacGroup World Area Code Group - 

24 TkCarrierChange Ticketing Carrier Change Indicator - 

25 TkCarrierGroup Ticketing Carrier Group - 

26 OpCarrierChange Operating Carrier Change Indicator - 

27 OpCarrierGroup Operating Carrier Group - 

28 RPCarrier Reporting carrier code - 

29 TkCarrier Ticketing carrier code - 

30 OpCarrier Operating carrier code - 

31 BulkFare Bulk fare indicator - 

32 Passengers Number of passengers - 

33 MktFare Market fare (ItinYield*MktMilesFlown) - 

34 MktDistance Market distance Includes ground 

transport

35 MktDistanceGroup Distance group, in 500 mile intervals - 

36 MktMilesFlown Market miles flown (track miles) - 

37 NonStopMiles Non-Stop Market Miles (Radian Measure) - 

38 ItinGeoType Itinerary Geography Type - 

39 MktGeoType Market geography type - 



7 http://www.transtats.bts.gov/Fields.asp?Table_ID=247
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

97

T-100 Columns8:

Number Column Description 

1 Year Year 

2 Quarter Quarter (1-4) 

3 Month Month

4 AirlineID US DOT ID number 

5 UniqueCarrier Unique carrier code 

6 Carrier IATA carrier code 

7 FlightDate Flight date (yyyymmdd) 

8 DayofMonth Day of month 

9 DayOfWeek Day of week 

10 Flights Number of flights 

11 FlightNum Flight number 

12 TailNum Tail number 

13 AirTime Flight time (minutes) 

14 ArrDel15 Arrival delay indicator, 15+ min. 

15 ArrDel30 Arrival delay indicator, 30+ min. 

16 ArrDelSys15 Arrival delay indicator, 15+ min. 

17 ArrDelSys30 Arrival delay indicator, 30+ min. 

18 ArrDelay Arrival delay (minutes) 

19 ArrTime Actual arrival time (hhmm) 

20 ArrTimeBlk CRS arrival time block, hourly intervals 

21 CRSArrTime CRS arrival time (hhmm) 

22 DepDel15 Departure delay indicator, 15+ min. 

23 DepDel30 Departure delay indicator, 30+ min. 

24 DepDelSys15 Departure delay indicator, 15+ min. 

25 DepDelSys30 Departure delay indicator, 30+ min. 

26 DepDelay Departure Delay (minutes) 

27 DepTime Actual departure time (hhmm) 

28 DepTimeBlk CRS departure time block, hourly intervals 

29 CRSDepTime CRS departure time (hhmm) 

30 Origin Origin airport 

31 OriginCityName Origin airport, city name 

32 OriginState Origin airport, state code 

33 OriginStateFips Origin airport, state fips 

34 OriginStateName Origin airport, state name 

35 OriginWac Origin airport, world area code 

36 Dest Destination airport 

37 DestCityName Destination airport, city name 

38 DestState Destination airport, state code 

39 DestStateFips Destination airport, state fips 

40 DestStateName Destination airport, state name 

41 DestWac Destination airport, world area code 

42 Distance Non-stop distance (using radian measure) 



8 http://www.transtats.bts.gov/Tables.asp?DB_ID=120
A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions Purdue-IAI Mtroplex Team 

98

43 DistanceGroup Distance intervals (250 miles) 

44 TaxiIn Taxi in time (minutes) 

45 TaxiOut Taxi out time (minutes) 

46 Off Off time (hhmm) 

47 On On time (hhmm) 

48 Cancelled Cancelled flight indicator 

49 CancellationCode Reason for cancellation 

50 Diverted Diverted flight indicator 

51 CarrierDelay Carrier delay (minutes) 

52 WeatherDelay Weather delay (minutes) 

53 NASDelay NAS delay (minutes) 

54 SecurityDelay Security delay (minutes) 

55 LateAircraftDelay Late aircraft delay (minutes) 
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