October 2010 

NASA/CR–2010-216853 

Analysis of Dependencies and Impacts of 

Metroplex Operations 

Daniel A. DeLaurentis and Sricharan Ayyalasomayajula 

Purdue University, West Lafayette, Indiana 
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October 2010 

NASA/CR–2010-216853 

Analysis of Dependencies and Impacts of 

Metroplex Operations 

Daniel A. DeLaurentis and Sricharan Ayyalasomayajula 

Purdue University, West Lafayette, Indiana 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 
1

Fig. 1. Notional representation 

of service between NYNJ and 

other airports in NAS

1 Introduction

This report documents research performed by Purdue University under sub-contract to the George Mason University 

(GMU) for the Metroplex Operations effort (under NASA contract number NNX07AT23A) sponsored by NASA‘s 

Airportal Project (Subtopic 20 in the NASA Research Announcement NNH06ZEA001N). Purdue University 

conducted two tasks in support of the larger efforts led by GMU: a) a literature review on metroplex operations 

followed by identification and analysis of metroplex dependencies, and b) the analysis of impacts of metroplex 

operations on the larger U.S. domestic airline service network. The tasks are linked in that the ultimate goal is an

understanding of the role of dependencies among airports in a metroplex in causing delays both locally and networkwide. Put simply by the FAA*

: ―Congestion, airports in close geographical proximity, and other limiting factors

reduce efficiency in busy Metroplex airspace.‖ As such, the Purdue team has formulated a system-of-systems 

framework to analyze metroplex dependencies (including simple metrics to quantify them) and develop compact 

models to predict delays based on network structure. These metrics and models were developed to provide insights 

for planners to formulate tailored policies and operational strategies that streamline metroplex operations and 

mitigate delays and congestion.

Metroplex airports share major portions of both airspace resources and local population from which demand 

emanates. Coordination among and competition for the limited number of operations into and out of a metroplex 

occur in the presence of dependencies between these airports. When demand outstrips available resources under a 

given operational strategy, delays result. Further, delays occurring at a metroplex‘s airports propagate across the 

National Airspace System (NAS) via airline service networks. The graphic in Fig. 1 is a schematic of the New 

York/New Jersey metroplex (NYNJ) that illustrates how airports interact with one another (via dependencies) as 

well as with the other airports in the NAS (via airline service networks). The dashed-oval depicts the metroplex‘s 

airspace with the arrows notionally representing the dependencies between EWR, JFK and LGA (denoted by 

squares). These dependencies arise from numerous categories such as the arrival and departure fixes in use, 

policy/regulatory issues such as capacity limits on the airports, and economic issues such as landing fees. At the 

same time, NYNJ airports interact with the other airports (circles) in the NAS via schedules airline service (denoted 

by solid lines). These flights are allocated and managed by different airlines 

that are competing with each other for the same market share, thus adding 

another dimension to the complex couplings between a metroplex‘s airports. 

Therefore, it is imperative to also understand the topological properties of the 

different airline networks serving a metroplex and the dynamics of their 

interactions to gain a holistic view of what causes a metroplex to have 

congestion and flight delays. As documented later in this report, statistical 

properties of service networks of different airlines were studied, leading to a

regression-based model developed to estimate the number of flights delayed at 

a metroplex. The model attempts to correlate the topological properties of 

airline service networks with the number of delayed flights at an airport in a 

year. The model has been further refined to also estimate the number of 

monthly delays at an airport. Work is underway to correlate the fluctuation in 

delays with seasonal variations in weather and traffic volume. The details of 

the framework, dependency metrics, studies of airline networks, and the delay 

estimation models are provided in the following sections.

 

*

―FAA Response to the Recommendations of the RTCA NEXTGEN Midterm Implementation Task Force,‖ 

http://www.faa.gov/about/initiatives/nextgen/media/FAA_TASKFORCE_RESPONSE_1-31-2010.pdf
2

2 Technical Approach

 2.1 Foundation for network and dependency analysis: A System-of-Systems

framework

A system-of-systems (SoS) is a collection of independent, often heterogeneous, components that collaborate via 

interacting networks to produce some unique capability (ref. 1). Since each entity has both independent and 

collaborative operations scenarios, conflict in their objectives may arise. Applicability of the system-of-systems 

approach to study the air transportation system was demonstrated in (ref. 2). Therefore, this approach was adopted to 

analyze metroplex operations in the current project. The decomposition in Table 1 is a result of this effort and is 

essential to 1) identify the hierarchy amongst the various networks in the NAS (rows in the chart), and 2) understand 

how the interactions within each of these networks across the different dimensions (columns), in combination with 

different stakeholder interests, affect a metroplex‘s operations. Additional descriptions of the dimensions and the 

significance of the vertical decomposition are available in (ref. 3 and 4).

A graphical depiction of hierarchical SoS relationships in air transportation with focus on metroplex is displayed in 

Fig. 2. Interactive relationships between components are readily apparent. For example, airlines serving metroplex 

airports interact with one another to enhance their business and increase their net earnings (β- and γ-levels in Fig. 2); 

airports are nodes and the flights between them are links in this network. On the other hand, studying a composite 

flight network combining the service networks of all airlines provides insights into NAS-wide traffic between the 

various airports (δ-level in Fig. 2). Another level of abstraction of this network leads to a scenario wherein 

metroplex airports are replaced with  ̳modules‘ that are connected to the other NAS-wide metroplex modules and 

airports (the γ-level in Fig. 2). Airports within each module are dependent on each other via dependencies, where as 

they are connected to the remainder of NAS by means of airline service networks. Recalling Fig. 1, the  ̳external‘ 

interactions occurring between other airports and metroplex modules in NAS are different from those  ̳internal‘ to 

the metroplex module.

Table 1. A System-of-systems Definition Matrix with Focus on Metroplex

System-of-systems Dimensions

Resources Operations Policy Economics

Layers of Networks

ε

Global Air 

Transportation

Global airline 

operations

Bilateral agreements, ICAO 

regulations etc.

WTO; Global 

Marketplace

δ

National Air 

Transportation 

System

All airlines at all 

airports in the US

National Air Transportation 

System policies

Forecasts of National 

Air Transportation

γ

Metroplex and 

non-Metroplex 

airports

One airline‘s 

operations at all 

airports

Policies concerning slots, codesharing etc. in a metroplexEconomics of an airline 

and metroplex

β Airports One airline‘s 

operations at an airport

Policies concerning aircraft 

operations, noise regulations, 

use of airport resources etc.

Economics of building, 

operating, leasing etc. 

of airports and airport 

resources

α

Aircraft, 

runway, ATC 

etc.

Aircraft movements on 

runways

Policies concerning airport 

runway and surface operations

Economics of building, 

operating, leasing etc. 

of runways and aircraft

In order to understand  ̳internal‘ interactions, metrics to quantify the dependencies between metroplex airports were 

developed. In order to understand  ̳external‘ issues, the topology of the network of flight operations between a 

metroplex and the other airports was analyzed. A composite network model was constructed by combining the 

service networks of various airlines serving a metroplex, and their statistical properties were studied. The results 

were used to assess impact of different operational and regulatory scenarios in airline operations (with airline 

profitability coming from GMU analysis) on the overall performance of NAS. The graphic in Fig. 3 is a framework 

being developed for evaluating the impact of various metroplex operational strategies. It is based on the hypothesis 
3

that an understanding of the dependencies in a metroplex and the interactions between the overall service network 

and the metroplex‘s operations is essential to developing appropriate congestion mitigation strategies for the 

metroplex. As part of this, the impact of these strategies on the various stakeholders is also studied. The notional 

Pareto chart in the lower right corner of Fig. 3 compares the effects of congestion mitigation solutions, such as slot 

controls, new runways, etc., on the overall service network and airlines. Candidate measures for evaluating the 

performance of the overall service network are the number of delayed flights, passenger throughput etc., while they 

are profitability, number of markets served, etc. for airlines. The remainder of this section describes the process

adopted to characterize and analyze metroplex dependencies, and an analysis of the interactions between the overall 

service network and metroplex operations.

Fig. 2. System-of-Systems Depiction of Hierarchical Interactions in NAS

Fig. 3. Framework for integrated analysis of metroplex operational strategies
4

 2.2 Characterization of Metroplex Dependencies

Metroplex dependencies occur across different dimensions between the constituent airports (Table 2). For example, 

passengers flying into LGA for their transatlantic flight from JFK require a means of transferring between these 

airports, possibly via a ground-link. In this case, JFK and LGA have an operational dependency to meet passenger 

needs. On the other hand, the NYNJ airports are owned and managed by the Port Authority of New York and New 

Jersey (PANYNJ) (ref. 5) As a result, these airports can now be considered as a collective entity that is trying to 

sustain itself economically while fulfilling its primary purpose of providing safe and reliable air transportation. That 

is, the NYNJ airports have economic and regulatory dependencies stemming from a single owner (PANYNJ). Other 

metroplexes will have different economic dependencies as their ownership and management structures vary. In 

either case, the controlling agencies enact regulations and policies that can affect all airports in a metroplex, and 

there can be strong cross-effects between the various dimensions of dependencies in the metroplex. For example, 

imposing slot-controls at one airport can alter the nature and form of operational and economic dependencies 

between the airports (different arrow-heads and solid and dashed-lines between NYNJ airports in Fig. 1).

Table 2. Dimensions of metroplex dependencies

Dimension Example Attributes

Resource Airport proximities, runway configurations, airspace geometry

Operational Airport operations, airlines, capacities, passenger ground-link

Policy/Regulatory Slot-controls, congestion-pricing, noise regulations, carbon credits

Economic Ownership of airports, landing fees, lease of terminals

Airline operations contribute toward operational dependencies. Though these airports serve the same metropolitan 

area, airlines often consider them as separate markets. Consequently, airline competition drives operational 

redundancies by serving the same set of markets from multiple metroplex airports. Data was collected to identify 

markets with at least 200 annual flights to any of the three major NYNJ from 2000 to 2006.The chart in Fig. 4 shows 

the fraction of these markets that have service to all three NYNJ airports). Across the seven year span presented, this 

fraction was typically about 30% (though it has steadily risen since 2001). Appendix C examines another form of 

operational dependency at the individual airline level in NYNJ.

Fig. 4. Fraction of markets common to EWR, JFK and LGA in NYNJ (%)

2.2.2 Dependency metrics

Formulation of dependency metrics focused first on the resource and operational dimensions. Two types of 

dependency analyses were conducted to form metrics: constructive and observational/interpretive. Constructive 

analysis deals with the physical resources of a metroplex airport (e.g., runway configurations) and their influence on 

operations at the metroplex. Observational/Interpretive analysis uses operational data to determine the impact of 

dependencies (and their interaction) on metroplex operations.
5

2.2.2.1 Constructive Analysis: Coarse-grain dependency metrics

While it is clear that complicated dependencies may exist in the metroplex airspace, the airspace utilization is driven 

by the number of runways, their orientation, and airport proximity. Thus, a ―course-grain‖ metric was developed to 

analyze how runways influence traffic patterns, congestion and delays, and also to serve as a qualitative study of 

how airport infrastructure influences its operations. Metroplex runway configuration, usage, and proximity were

considered to develop these metrics. 

A reference runway was defined within the metrics to capture the runway dependencies. The runway with the most 

traffic in a year was chosen as the reference runway; other runways are compared to develop the intermediate 

components of the dependency metrics (Table 3). Data for traffic on each runway was estimated from the FAA‘s

Aviation System Performance Metrics (ASPM) (ref. 6).

Table 3. Coarse-grain dependency metrics for runways at an airport, an airport, and a metroplex

Metric Property Definition

Runway length 

dependency (dl(i))

Estimate of how runway 

length affects runway flight 

traffic volume

dl(i) = 1.0; when li/ lRR < 0.6

 0.9; when 0.6 ≤ li/ lRR < 0.8

 0.6; when 0.8 ≤ li/ lRR < 0.9

 0.3; when 0.9 ≤ li/lRR

Runway orientation 

dependency (do(i))

Estimate of how runway 

orientation affects runway 

traffic volumes 

Parallel runways have least 

interference and least 

dependency

do(i) = 0.1; rwyi parallel to RR

 1.0; rwyi perpendicular to RR

 0.5; rwyi approx. 45° to RR

Runway proximity 

dependency (dp(i))

Estimate of how runway 

proximity affects runway 

traffic volumes

dp(i) = (Distance to RR) / 

(max. runway to runway distance in 

airport)

 = di,RR / max. (di,j)

Runway Dependency 

Metric of runway i

(RDMi)

Composite dependency 

metric for runway i

RDMi = dl(i) * do(i) * dp(i)

Runway Dependency 

Metric of an airport 

(RDM-A)

Composite of all runway 

dependencies at an airport

Traffic on each runway used 

as weights to capture the 

runway‘s  ̳contribution‘

RDM-A = Σ (Rwy_opsi * RDMi) / (Total 

airport operations)

where Rwy_opsi depicts the number of 

flights operated from runway i

Runway Dependency 

Metric of a metroplex 

(RDM-M)

Similar to RDM-A, but 

extended to a metroplex

RDM-M = Σ (Rwy_opsi * RDMi) / (Total 

metroplex operations)

RR – Reference Runway; rwyi – runway i; li – length of rwyi; di,j – distance between rwyi and rwyj;

di,RR – distance between centers of rwyi and Reference Runway (RR); dl(i) – length dependency of rwyi;

do(i) – orientation dependency of rwyi; dp(i) – proximity dependency of rwyi;

Rwy_opsi – number of flights operated from rwyi
6

The dependency metric of a runway was defined as the product of the individual metrics and was combined with the 

traffic on each runway to compute an airport‘s dependency based on the dependencies between its runways; hence 

the terms  ̳Runway Dependency Metric of a runway‘ and  ̳Runway Dependency Metric of an airport.‘ The airport 

metric was extended to a metroplex by treating a metroplex as a very large airport with its runways widely separated 

from one another. The reference runway in this case was the runway with the most traffic in the metroplex. A high 

value of the metric indicates larger degree of dependency.

 2.2.2.2 Observational Dependency Analysis: Capacitated metroplex operations

In principle, by observing the recorded data on operations at metroplex airports, one could conceive of a capacity 

diagram for a metroplex similar to those constructed for airports. Airport capacity diagrams display departure 

operations vs. arrival operations and each data point in the diagram plots the number or arrivals and departures in a 

given hour period. The particulars of an airport are also used to generate boundary lines on the diagram above which 

operations are not deemed safe at that airport. Construction of a capacity diagram for a metroplex is conceivable but 

was beyond the scope of this study. However, an initial step in this direction was conducted via identification of the 

―boundary violations‖ for NYNJ by finding one hour periods when all three major airports were at, or over, their

capacity boundaries. By examining these instances, the nature of operational dependencies between airports can be 

investigated more clearly in the future. The method and the NYNJ example are presented next.

The aforementioned ASPM database was queried to find ―what was happening‖ at JFK during one hour periods 

when both LGA and EWR were at or over capacity in 2007 and 2008. There were four instances (out of 8760 

possible one hour periods per year) in 2007 and two in 2008 when EWR and LGA were at or beyond their 

predetermined capacity limits while JFK was well below its capacity. The fact of very few occurrences is due to 

operations caps put in place at LGA in 2007; with operations caps, LGA is rarely in violation of its capacity limits. 

The values in Table 4 depict the number of one hour periods in 2007 and 2008 when operations at LGA were below 

capacity, at the same time that EWR and JFK were at or beyond their nominal hourly capacities. The capacity curves 

for each of the three airports are shown in Fig. 5 (for 2007) and Fig. 6 (for 2008) along with the data for the 

instances mentioned in Table 4 (each instance is represented as a red circle). In 2007, there were 248 occurrences

and 19 in particular that seemed to have reduced performance (actual ops less than scheduled), indicating an 

implication of operational dependency. When the other two major airports are capacitated, the operations-capped 

LGA still was prone to feeling the effects. In 2008, operations caps were also imposed at EWR and JFK; thus, you 

see only one occurrence in the entire year. This does not mean that dependencies are eliminated; just that the caps 

are working in terms of prohibiting airports from operating at levels beyond what is attainable. Capacity diagrams 

for a metroplex, similar to those used for every airport, can be very valuable for decision makers to develop effective 

policies to better manage the metroplex‘s resources. Details of the attempts to compute metroplex capacities and 

develop metroplex capacity diagrams are provided in the results section.

Table 4. Number of below-capacity hours at LGA while EWR and JFK were at or beyond their capacity

Year No. of total 

occurrences

Actual flights (arr/dep) were fewer than scheduled by at least 8

No. of occurrences Time of occurrence

2007 248 19 3PM and 4PM; Mostly Jul. and Aug.

2008 1 0 --
7

Fig. 5. Instances in 2007 when LGA was operating below its normal hourly capacity while EWR and JFK 

were at or beyond their normal capacities. Numerical values not shown for clarity.

Fig. 6. In 2008, there was only one instance when EWR and JFK were at or beyond their normal capacities

(LGA was below capacity at this instance). Numerical values in LGA data point are: (Actual Arrivals, Actual 

Departures)(Scheduled Arrivals, Scheduled Departures) YearMonth-Day-Hour of the day 
8

 2.3 Characterization of Network-effects: Delay prediction model

As illustrated notionally in Fig.1, the external interactions associated with a metroplex are primarily via the markets 

served by the airlines. Changes to the flights departing and arriving from a metroplex have consequences in the NAS 

network. GMU teammates investigated the operations of a conceptual benevolent monopolist airline operating at 

one of the three major NYNJ airports. Such an airline has a monopoly at this airport but is  ̳non-selfish‘ in the sense 

that it optimizes its schedules to increase its profits while satisfying as much of the demand as possible. Optimized 

schedules were developed by GMU for this Benevolent Monopolist Airline (BMA) under different scenarios of fuel 

price and capacity limits at the metroplex airport. The desire to know the network-wide implications of such a new 

schedule necessitates the need for a compact model of network performance.

The profit from these optimized schedules comprises airline profitability in the Pareto chart of Fig. 3. In order to 

understand the interaction of these schedules with the composite flight network (described above), and to study the 

impact of BMA‘s decisions on the performance of the NAS, a regression-based delay prediction model was 

developed (ref. 9). The model estimates the number of flights delayed at an airport based on its  ̳role‘ in the 

composite flight network—a delayed flight is an arrival or departure that was delayed by more than 15 min. 

Statistical properties of the composite network were measured via tools from network theory (ref. 7, 8 and 9). The 

model was based on parameters describing the different properties of the service network‘s topology: the criticality 

of an airport (a node) in preventing or causing cascading delays, the importance of the connectivity between two 

airports (a link) and how well-connected an airport is (a regional airport, hub or a super-hub). The population served 

by an airport was also included as a variable in the model to understand its role in transforming an airport from a 

regional airport status to a hub and/or a super-hub. A glossary of the network parameters used in the model is given 

in Appendix E.

Equation (1) describes the general structure of the delay-prediction model. The variables in bold-type font are 

various parameters representing an airport‘s network statistics and other characteristics. All variables were 

normalized to reduce difficulties caused by differences in scale with the statistics of the airport with the highest 

number of delays. For example, Chicago O‘Hare International (ORD) had the highest number of delays in 2007 (ref. 

6). Therefore, to develop a model for estimating the number of delayed flights at an airport in 2007 all variables 

were normalized with respect to ORD‘s network statistics (Eq. (2)). 

 * *

 * * *

dom cc evc

pop intl ref

Delayed Ops c c c

c c n

2

(constant dom_traffic + *clus_coeff eig_vec_centrality 

population international)

(1)

2

(0.05075 0.83866* 0.00474* 0.13504*

 0.10148* +0.03273* ) *219121

Delayed Ops = dom_traffic clus_coeff eig_vec_centrality

population international 

(2)

 2.3.1 Data collection, assumptions and model validation

To develop the model in Eq. 2, data was collected from the Bureau of Transportation Statistics (BTS) database for 

all domestic airports in 2007 (ref. 10). Examples of data collected include: the number of arrivals and departures 

between any two airports, the number of arrival and departure delays at an airport, and the number of destinations 

directly connected to an airport. The service network thus generated was a composite of the service networks of all 

airlines; the focus of this model was on the overall connectivity between airports rather than on airline competition 

for the market share at these airports. The population data was obtained from the U.S. 2000 Census Survey (ref. 11); 

the population within a sixty mile radius of an airport was considered to be  ̳served‘ by the airport.

There are over 1,200 airports in the U.S. and the computational challenge to analyze the interactions between all 

these airports was found to be formidable. Further, only about 25% of these airports account for the entire domestic 

flight traffic. Consequently, a set of 268 airports was chosen to conduct all analyses. The set was compiled by 

analyzing the U.S.-carriers-only T-100 domestic segment data from BTS for the years 2002 to 2007 (ref. 10). An 

individual list of airports contributing to 94% of total annual domestic commercial flights was compiled for each of 

the years from 2002 to 2007. Those airports that appeared in at least four out of these six years were chosen to form 

the set of 268 airports. This final set presented a much smaller network to analyze while contributing to at least 94% 

of domestic commercial traffic. This has strong implications on all analyses in that this set can be considered as a 

 ̳surrogate‘ to the NAS for studying the various policy and operational scenarios—only a few airports  ̳making‘ or 
9

 ̳breaking‘ a regulation or operational change with nation-wide impacts affords policy makers the freedom to focus 

on only this small set rather than the entire NAS. Such a set also loosely follows the logic behind more well-known 

datasets such as OEP 35, OPSNET 45, and LMINET 102 in that all of them present a smaller set of variables in 

order to focus on broad, sweeping impacts.

The model in Eq. (2) can be employed to estimate the number of delayed flights at an airport in a year, as long as a 

new composite service network is  ̳similar‘ in its topological structure to the base network used to generate the 

model. For example, the new network can be a variation of the base network in terms of small changes to the 

number of markets served and traffic on each route, the population using an airport, the fleet mix on a route, and so 

on. These new networks can be candidate solutions ―designed‖ to achieve improved passenger/flight throughput, 

higher revenue/profits for airlines and airports, reduced delays at selected airports, etc., and Eq. (2) can be employed 

to predict the number of delayed operations at each of the airports within the new networks.

The graph in Fig. 7 is a comparison between actual data and the values predicted by the delay-prediction model for 

the number of flight delays at the set of 268 airports for 2007. The small circles denote airports, with some of them 

identified individually as belonging to NYNJ (triangle), NorCal (bigger circle) and SoCal (diamond) metroplexes. A 

±5% error band is also shown in the graphic to enhance the comprehension of the model‘s behavior. On the whole, 

the model presents a tool with satisfactory error levels to estimate the number of delayed flights at the major 

airports. This is a very important result in that the model can be employed to evaluate the various candidate 

networks from the perspective of various stakeholders—higher passenger and flight throughput, reduced congestion 

and delays, smaller turn-around times for flights etc. for airports; operations and schedules yielding increased 

profits, higher load-factors, bigger market-share etc. for airlines, and so on.

0.00E+00

5.00E+04

1.00E+05

1.50E+05

2.00E+05

0.00E+00 5.00E+04 1.00E+05 1.50E+05 2.00E+05

Predicted Values -- Number of Delayed Flights in 2007

Actual Data -- Number of Delayed Flights in 2007

Validation of 2007 Annual Delay Prediction Model

Predicted & Corrected

NYNJ

NorCal

SoCal

Fig. 7. Validation of annual airport delay-prediction model for 2007 against actual data
10

3 Results

3.1 Characterization of dependencies

 3.1.1 Study of analogous metroplexes

In addition to NYNJ, several other metroplexes were analyzed to understand how dependencies differ from one 

metroplex to another and as a precursor to study of how generic solutions to minimize congestion and delays 

perform for different metroplexes. These other metroplexes included: Northern California (SFO/SJC/OAK), 

Southern California (LAX/LGB/ONT/SNA), Chicago (ORD/MDW/MKE), Miami (MIA/FLL/PBI), and 

Washington, DC (DCA/IAD/BWI).

Data were collected to compare these metroplexes across the different dimensions of dependencies (a subset of 

information gathered is shown in Table 5), including: 1) operational issues—proximity of metroplex airports, 

runway configurations (length and direction), operational capacity (number of flights, passenger throughput, gates, 

etc.); 2) economic issues—ownership of the airports (single/multiple, public/private); 3) regulatory/policy issues—

the TRACON they belong to, airport capacity limits, etc.; and 4) type of airlines operating at the metroplex 

(domestic/international, legacy/regional etc.).

Table 5. Data fields employed in comparing different metroplexes

Data fields NYNJ NorCal Chicago

1. Ownership of airports Port Authority of New 

York and New Jersey

SFO—City & County of 

San Francisco;

OAK—Port of Oakland;

SJC—City of San Jose

ORD & MDW—City of 

Chicago;

MKE—Milwaukee County

2. TRACON Ronkonkoma, NY 

(ZNY) Sacramento, CA (NCT)

ORD & MDW—Elgin, IL 

(C90);

MKE—Milwaukee, WI

3. Airport proximities 

(mi.)

JFK-EWR (21);

JFK-LGA (11); 

EWR-LGA (16.5)

SFO-OAK (11.3);

SFO-SJC (30.4);

OAK-SJC (29.6)

ORD-MDW (15.5);

ORD-MKE (67);

MDW-MKE (80.8)

4. No. of gates JFK (106), EWR (103), 

LGA (74)

SFO (94), OAK (30), 

SJC (32)

ORD (178), MDW (43), 

MKE (45)

5. No. of flights (domestic 

and intl. flights of US 

carriers only in 2007)

JFK (326,973); 

EWR (365,2); 

LGA (335,89)

SFO (282,143);

OAK (164,819)

SJC (118,504);

ORD (799,204);

MDW (194,284);

MKE (145,722)

After preliminary analysis, Northern California (NorCal) and Southern California (SoCal) metroplexes were chosen 

for further study. An initial analysis showed that while both NorCal and SoCal differed from NYNJ in having 

multiple entities with ownership of the airports, NorCal was similar to NYNJ in the geographical layout of an 

approximate triangle (Fig. 8).
11

a. New York/New Jersey 

metroplex

b. Northern California metroplex c. Southern California metroplex

Fig. 8. Schematic of three metroplexes comparing positioning of their constituent airports

The network of airports connected to NorCal and SoCal was analyzed and compared to NYNJ (Table 6). For this 

purpose, composites of service networks of all airlines servicing the metroplexes were generated. Only those airports 

that had at least 10 flights to and from these metroplexes from June to August 2007 were included (first column in 

Table 6). NYNJ airports had about 34% of overlap in the markets served (Fig. 4), as did NorCal, but the overlap was

only 8% for SoCal for the same period. This time period was chosen to study the operations at other metroplexes

when NYNJ airports were experiencing the worst delays and congestion in recent years (ref. 12). The results of this 

comparative study showed that NorCal was more similar to NYNJ in terms of network cohesiveness (average 

clustering coefficient of metroplex airports—third column in Table 6) while SoCal was more similar in terms of 

network size (113 airports inclusive of metroplex airports) for NYNJ compared to 103 for SoCal. NorCal was 

chosen as the analogue metroplex due to the similarity in relative position of its airports to NYNJ and also owing to 

the extent of market overlap amongst its airports. For more details about the different network parameters in Table 

6, refer to Appendix E.

Table 6. Network analysis of different metroplexes – June–August, 2007

No. of airports 

in the network

Avg. 

degree

Avg. Clust. 

Coeff. (CC)

Avg. deg. of 

metroplex airports

Avg. CC of 

metroplex airports

NYNJ 113 30 0.74 61 0.48

NorCal 85 21 0.64 38 0.44

SoCal. 103 26 0.61 49 0.59

 3.1.2 Coarse-grain dependency metrics: runway-dependency metrics

Results from computing coarse-grain dependency metrics via the two formulas in Sec. 2.2.2 are shown in Table 7

and Table 8. The metrics were computed for a six year period from 2002 to 2007. In addition to the NYNJ and 

NorCal metroplexes, the team also chose Hartsfield-Jackson Atlanta International (ATL) and Denver International 

(DEN) airports as part of the study to understand how the metric reflects reality at an airport that dominates the local 

TRACON airspace. ATL and DEN each have operations comparable in volume to JFK and SFO (NYNJ and NorCal

metroplexes, respectively), each have multiple airlines operating out of them, and each (especially ATL) face

problems of delays and congestion.
12

Table 7. Runway dependency metrics for NYNJ and NorCal

Year

Metroplex traffic in 2007 Reference Runway RDM-M (dl*do*dp)

NYNJ NorCal NYNJ NorCal NYNJ NorCal

2007 1,233,473 697,489 JFK 31L SFO 28R 0.359 0.08

2006 1,184,445 678,501 EWR 22R SFO 28L 0.191 0.085

2005 1,141,250 666,660 EWR 22L SFO 28R 0.177 0.08

2004 1,113,671 673,929 EWR 22R SFO 28R 0.199 0.08

2003 1,016,652 619,400 EWR 22L SFO 28R 0.197 0.062

2002 913,420 620,531 EWR 22L SFO 28R 0.206 N/A

Table 8. Runway dependency metrics for ATL and DEN

Year

Airport traffic in 2007 Reference Runway RDM-A (dl*do*dp)

ATL DEN ATL DEN ATL DEN

2007 962,390 594,001 ATL 27R DEN 17R 0.024 0.051

2006 945,018 570,757 ATL 27R DEN 8 0.024 0.103

2005 945,946 535,267 ATL 27R DEN 8 0.049 0.088

2004 936,677 534,661 ATL 27R DEN 17R 0.049 0.066

2003 874,647 474,688 ATL 27R DEN 17R 0.049 0.061

2002 836,262 397,938 ATL 27R DEN 17R 0.049 0.058

 3.1.2.1 Comparison of dependencies in NYNJ and NorCal via dependency metrics

From Table 7 it can be observed that dependencies at NYNJ are larger than those at NorCal, driven by greater traffic 

and fewer parallel runways than NorCal. Further, there are two major domestic and international hub airports in 

NYNJ (EWR and JFK) compared to only one in NorCal (SFO), resulting in more dependency interactions between 

the constituent airports. Metric values also varied with the choice of reference runway (RR). For NYNJ, from 2002 

to 2006, the RR was located at EWR, shifting to JFK in 2007. This was due to a significant increase in JFK traffic in 

2007. As a result, the runway proximity dependency became a major factor in affecting this change (Table 3). 

Further analysis needs to be conducted to understand if this mechanism influenced dependencies at NYNJ in 2008 

and 2009 as well. For NorCal, ASPM data was not available to estimate operations per runway for OAK and SJC in 

2002, and as a result, the corresponding dependency metrics for NorCal were significantly different from other years

in the period of study. Consequently, the analysis period for NorCal starts from 2003.

In the case of non-metroplex airports, the dependency metrics (though very small in magnitude compared to NYNJ)

decreased for ATL from 2005 to 2006 (Table 8). This decrease was due to the addition of runways 10/28 at ATL in

2006, and indicates that runways were less dependent on each other starting from 2006, or, operations at ATL were 

more  ̳decoupled‘ from 2006. On the other hand, there was some variation in the dependencies at DEN from 2005 to 

2007, peaking in 2006. A likely reason is that a new runway was added to DEN in 2004, prompting a realignment of 

traffic across the various runways—runway 8 was the RR for 2005 and 2006 compared to runway 17R for the 

remaining period of this analysis. Analysis of 2008 data has to be completed to verify if this stable trend continued.

An interesting observation was the apparent similarity between NorCal and DEN (Table 7 and Table 8). This is most 

likely due to the fact that SFO is the most dominant airport in terms of volume of operations in NorCal (as opposed 

to NYNJ where both EWR and JFK have similar number of operations), causing NorCal to be influenced by SFO 

and thereby resembling a hub-airport. Further, SFO‘s runway configurations are more similar to DEN than ATL 

(where all runways are parallel to one another), and has resulted in NorCal being more similar to DEN than ATL.

These results illustrate the RDM-M metric appears to reflect operational changes that affect dependencies; however,

much work remains in understanding dependencies both at an airport and at a metroplex level. Alternative ways to 

quantify dependencies are currently being explored, such as redefining the metrics without the use of a reference 

runway, or using a different definition for a reference runway.
13

 3.1.3 Observational dependency analysis

 3.1.3.1 Computing metroplex capacity

To compute metroplex capacity, FAA Circular AC: 150/5060-5 was used (ref. 13). The Circular was issued to 

compute airport capacities and delays for different runway configurations for planning purposes. But this document 

dates back to 1983, and its applicability had to be validated for present-day airport operations. LGA was chosen as 

the airport for validating this document by comparing traffic data obtained from the BTS database for 2002 to 2007 

(Table 9) (ref. 10). The annual traffic projections for LGA using the Circular were widely different from historic 

data. While there were no details provided in the Circular as to how the empirical relations between runway 

configurations and airport traffic were developed, it is assumed that they were based on operational and safety 

standards in place in 1983 (date of the Circular). Refer to Appendix A for more details on how the document was

employed in Table 9.

Table 9. Validation of FAA Circular for LGA from 2002–2007

Year LGA Traffic (T-100) ASV projections from Circular

2007 377,077 225,000

2006 385,542 225,000

2005 384,906 225,000

2004 385,122 225,000

2003 358,248 225,000

2002 280,575 225,000

The team attempted to compute metroplex-wide annual capacity using the Circular by treating a metroplex as a very 

large airport with widely separated runways—NYNJ was chosen as the test case. The sum total of the traffic at its 

major airports was compared with the Circular‘s projections, but poor results similar to the LGA case were obtained. 

This implies that though the Circular offers a simple methodology to determine an airport‘s annual capacity, it

cannot be directly applied to a metroplex as demonstrated. This is in addition to the earlier stated fact that the 

Circular is based on operational standards and assumptions that are not applicable to current day operations. That is, 

the factors driving a metroplex‘s operations are much more complicated than those at an airport. This underscores

the earlier hypothesis that dependencies play a very important role and it is imperative to gain a good understanding 

of them to develop strategies for enhancing the metroplex‘s operational performance. Therefore, any methodology 

to determine a performance metric such as a metroplex‘s capacity should incorporate its dependencies. Further, the 

implications of such a methodology are far and many: metroplex planners would have better decision-support in 

attempting to increase capacity, reduce congestion, reduce operational costs and expenditure (new operational 

procedures, installing new equipment, etc.), and so on. Refer to Appendix B for more details on how the Circular 

was used to compute the annual capacity values for LGA and NYNJ.

 3.1.3.2 Correlation between dependency metrics and metroplex traffic data

The graphic in Fig. 9 shows how the runway dependency metrics for NYNJ varied with traffic from 2002 to 2007. 

Traffic data for the three major NYNJ airports were used here and were obtained from the BTS online database. The 

traffic data were normalized relative to the 2002 values. The same results for NorCal are shown in Fig. 10.
14

Fig. 9. Correlation between runway dependency metric for NYNJ and traffic data

Fig. 10. Correlation between runway dependency metric for NorCal and traffic data

The purpose of this correlation analysis was to determine if one could use the dependency metrics to measure 

changes in dependencies caused by changes in metroplex operations. From Fig. 9 it can be observed that while the 

traffic increased by about 20% from 2002 to 2004, the metric did not change significantly. Also, for a 10% increase 

in traffic from 2004 to 2006 (relative to 2002) the change in the metric was largely insignificant (except for a slight 

decrease in 2005). But from 2006 to 2007, for a 5% increase in traffic, the change in the metric was very significant. 

The shift in reference runway from EWR to JFK caused this change, as observed in Table 7. For NorCal, the data 

for 2002 was excluded due to difficulties in estimating the traffic per runway (Sec. 3.1.2). There was a slight 

decrease in traffic from 2004 to 2005 (Table 7). The correlation studies for NorCal did not elicit any discernible 

pattern. Again, this could be a result of the definitions of the dependency metrics, or other factors not accounted for. 

Similar results were observed in the case of ATL and DEN. Overall, it appears that the metric does have correlative 

value when there is a shift in the reference runway, but less so for simple (and modest) growth in traffic.

3.2 Characterization of Network-effects

 3.2.1 Trade-studies: Optimized operations of Benevolent Monopolistic Airline

The impact of altering airline service networks connecting a metroplex to other airports in the NAS was analyzed to 

determine the impact on the overall performance of the NAS. Trade-studies were conducted to determine how 

changes in an airline‘s network properties affect its profitability on one hand, and system delays on the other 
15

(depicted by the notional Pareto chart in the lower right hand corner of Fig. 3). The delay-predictor model described 

in Sec. 2.3 was used to compute the number of delayed operations. Profits from the optimized schedules of the 

Benevolent Monopolist Airline (BMA) developed by the GMU team were used to represent airline profitability. The 

optimized schedules of BMA when operating at JFK are shown in Table 10. Optimized schedules were developed 

for three scenarios of 15 minute airport capacities for arrivals and departures at a fixed value of fuel price for the 

third quarter of 2007, 2008 and 2009. Similar schedules for EWR and LGA are shown in tables Table 11 and Table

12, respectively.

Table 10. Summary of optimized schedules for BMA for 3rd quarter in 2007, 2008 and 2009 at JFK

YearQuarter 2007Q3 2008Q3 2009Q3

Fuel Price 

($/gal.) 2.06 3.53 1.92

Operations 

per 15 min 18 20 24 18 20 24 18 20 24

Markets 46 46 46 47 45 46 53 53 53

No. of daily 

ops at JFK 560 614 622 530 530 560 606 666 686

Profit 

($M/Day) 3.825 4.748 4.870 4.030 4.089 4.137 4.153 4.667 4.766

Table 11. Summary of optimized schedules for BMA for 3rd quarter in 2007, 2008 and 2009 at EWR

YearQuarter 2007Q3 2008Q3 2009Q3

Fuel Price 

($/gal.) 2.06 3.53 1.92

Operations 

per 15 min 18 20 24 18 20 24 18 20 24

Markets 74 73 76 61 63 63 71 73 73

No. of daily 

ops at EWR 706 740 794 610 646 664 668 716 738

Profit 

($M/Day) 5.774 5.884 5.991 5.123 5.347 5.410 4.514 4.783 4.842

Table 12. Summary of optimized schedules for BMA for 3rd quarter in 2007, 2008 and 2009 at LGA

YearQuarter 2007Q3 2008Q3 2009Q3

Fuel Price 

($/gal.) 2.06 3.53 1.92

Operations 

per 15 min 16 18 20 16 18 20 16 18 20

Markets 59 59 61 58 59 59 60 60 60

No. of daily 

ops at LGA 784 834 862 700 718 726 764 810 832

Profit 

($M/Day) 3.981 4.087 4.119 3.178 3.205 3.219 3.860 3.938 3.968

The optimized schedules in Table 10 through Table 12 are for a quarter of a year, whereas the delay-predictor model 

in Eq. (1) was developed to compute annual delayed operations. As such, the model was modified to compute the 

number of quarterly delays. The model for 2007Q3 has an R2value of 0.96, 2008Q3 has an R2value of 0.95 and 

2009Q3 has an R2value of 0.94 (equations (3), (4) and (5), respectively). 

2

(0.05131 0.75656* 0.00570* 0.14713*

 0.09324* +0.01957* ) *62474

Delayed Ops = dom_traffic clus_coeff eig_vec_centrality

population international 

(3)
16

2

(0.02037 0.71607* 0.03140* 0.17373*

 0.11231* +0.09207* ) *44948

Delayed Ops = dom_traffic clus_coeff eig_vec_centrality

population international 

(4)

2

(0.03147 0.71625* 0.01757* 0.15094*

 0.05554* +0.04405* ) *53295

Delayed Ops = dom_traffic clus_coeff eig_vec_centrality

population international 

(5)

Results of the trade-studies between NAS-wide delays and BMA‘s profitability when operating from JFK are shown 

in Fig. 11. The number of delayed operations for the different optimized schedules did not differ significantly 

between each 15-minute capacity scenario for the third quarters of 2007 and 2009. This indicates that changes in 

BMA‘s schedules and the resulting changes to the composite service network were not of significant magnitude to 

have a marked impact on NAS-wide operations. This could also be a result of the structure of the delay-predictor 

model. As described earlier, the model was most sensitive to changes in flight frequency between airports, and 

predicts a greater number of delayed flights when there is an increase in an airport‘s flight traffic (equations (3) to 

(5)). Consequently, due to the increase in BMA‘s schedules at JFK (Table 10), the model predicted a corresponding 

increase in the number of delayed flights. Since BMA‘s monopoly was restricted to only one airport (JFK here)

operations at other airports in the NAS were not so affected as to result in a substantial NAS-wide increase in delays. 

This is more evident in Fig. 12, which compares the number of delayed flights at JFK against BMA‘s profitability; 

an increase in BMA‘s schedules at JFK resulted in a greater number of delayed flights. There was a small reduction 

in the number of delayed flights when capacity was increased from 18 to 20 flights per 15 minutes at JFK in 2008 

due to the fact that the number of BMA‘s operations at JFK stayed the same for these two scenarios. The apparent 

reduction can be attributed to 1) the change in schedules to some of the markets served by BMA, which slightly 

affected the composite service network properties, leading to a reduction in delays, or 2) the uncertainty in the 

delay-prediction model. Further study will illuminate the relative contribution of these two factors.

Results from similar trade-studies for EWR and LGA for the third quarters of 2007, 2008 and 2009 are shown in 

Fig. 13 and Fig. 14. The steeper slopes for EWR and LGA indicate a more pronounced (negative) correlation 

between airline profitability and delayed operations at those airports. To explore further, BMA‘s schedules were also 

analyzed to compute the change in airline profit vs. the change in the number of delays (Fig. 15). The purpose of this 

study was to understand the  ̳cost‘ of BMA‘s profitability. The change in delays and profit were computed with 

respect to the smallest per hour (or per 15 minute) airport capacity scenario at each airport—72 for JFK and EWR 

and 64 for LGA. It can be observed from Fig. 15 that in 2007, BMA‘s operations at LGA incurred the highest 

number of delays for every $1 million profit. And overall, EWR had the highest number of delayed flights for every 

$1 million profit of BMA, suggesting that the same kind of operational decisions by an airline can lead to different 

results for the stakeholders (airline and airport here). That is, certain policies or actions can be more effective at 

particular airports, and these same policies or actions can be detrimental at others. For the results in Fig. 15, JFK is 

the most favorable candidate in terms of the airline being profitable while causing fewer delayed flights compared to 

the other two airports—within the same metroplex, there is an asymmetry between the airports with respect to the 

outcome from implementing similar strategies.
17

Fig. 11. Trade-studies to analyze impact of BMA’s service on its profitability and NAS-wide delays when 

operating from JFK

Fig. 12. Trade-studies to analyze impact of BMA’s service on its profitability and JFK delays when operating 

from JFK
18

Fig. 13. Trade-studies to analyze impact of BMA’s service on its profitability and EWR delays when 

operating from EWR

Fig. 14. Trade-studies to analyze impact of BMA’s service on its profitability and LGA delays when operating 

from LGA
19

a. BMA operating from JFK b. BMA operating from EWR

c. BMA operating from LGA

Fig. 15. Trade-studies to analyze change in number of delayed flights with change in BMA’s profitability

4 Further Work

 4.1 Improved delay-prediction models

The delay models described earlier suffered from certain disadvantages. For example, the models did not include 

aircraft size as a predictor variable. The size of an aircraft affects separation requirements, which in turn affect the 

arrival and departure rates at an airport. Further, each aircraft size comes with advantages and disadvantages. 

Smaller aircraft feature shorter gate turn-around times and carry lower overhead costs, but are severely hampered in 

terms of the passenger volume they can transport. This can be mitigated only by using a large number of these 

aircraft, which in turn can saturate the airspace, leading to congestion and delays. On the other hand, larger aircraft 

can transport large volume of passengers but require wider separation distances, thereby reducing an airport‘s arrival 

and departure rates. Also, not all airports can handle large aircraft (heavy or super-heavy class). In addition to 

aircraft size, the models also exclude factors such as load factor, airport dependencies (either in terms of the metrics 

used to compute them as described earlier or in some other form), local weather, and so on. This section describes 

efforts currently underway to incorporate some of these factors and thereby improve the fidelity of the models.
20

 4.1.1 Estimation of monthly delays and effects of weather

The delay-prediction models described earlier were developed to estimate the number of annual and quarterly delays 

at an airport. Therefore, as a first step, the models are being modified to estimate the number of monthly airport 

delays, the premise being that an airport‘s traffic is not uniform throughout the year or a quarter—the busiest times 

of travel are the summer months of June through August and the holiday season from October to January, excluding 

occasional pockets of increased travel such as the Super Bowl, the Olympics etc. As such, policies and regulations 

enacted to mitigate congestion and delays should be sufficiently versatile to not only deal with these busy periods, 

but also to not impose additional constraints on airports and airlines during the off-peak periods. For example, 

imposing limits on airport capacity reduces delays and congestion, but can lead to under-utilization of capacity that 

could otherwise have been used to accommodate more flights, creating additional revenue to both airports and 

airlines. Another way to improve the fidelity of the models is to capture the effects of weather on air traffic by 

investigating if weather can be included as a predictor variable in the models or if it can be treated only as 

uncertainty. 

The graph in Fig. 16 depicts the number of monthly delayed flights at the OEP 35 set of airports as a percentage of 

the number of annual delayed flights at each of these airports in 2007; the black dashed-line represents the mean 

monthly values for OEP 35. The model under development attempts to estimate monthly variations in delays and 

use these estimates to predict delays in the following year. The flow chart in Fig. 17 describes the logic in 

developing this model. It consists of a longitudinal analysis of an airport, or a set of airports (such as OEP 35), to 

identify any discernible patterns in the number of monthly delayed flights. In Fig. 17, all months from 2002 to 2007 

are shown as an example period of analysis for developing a model that estimates the number of delayed flights in 

January, 2008. 

Fig. 16. Changes in airport delays within a year
21

Fig. 17. Logic to estimate monthly delays for OEP 35 airports for the following year by using current year’s 

data

The curve in Fig. 18 is a fitted trend-line for the number of domestic delayed flights at Chicago O‘Hare International 

Airport (ORD) in the month of January for the years 2002 to 2007. The curve followed a Fourier series:

coeffORD_Jan = a0 + a1 cos(ωx) + b1 sin(ωx)

where a0 = 10.26, a1 = 2.902, b1 = -1.761, ω = 1.694 

x = 1 for 2002, 2 for 2003... and 6 for 2007

Fig. 18. Trend in the number of delayed flights at Chicago O’Hare (ORD) in January from 2002 to 2007

The Fourier series shown in Fig. 18 depicts how the number of delayed flights in January at ORD varied from 2002 

to 2007 as a percentage of the total flights at ORD in the corresponding year. The new delay-prediction model under 

development makes use of such patterns for delays in particular months (e.g., January) and across all months (data 

shown in Fig. 16) at an airport (e.g., ORD) to predict the number of delayed flights at that airport at a future period 

(January 2008). This model is being developed as part of a framework for predicting monthly delays at a set of 

airports, instead of just at one airport. The focus is currently on OEP 35 airports to demonstrate the viability of this 

approach and to build confidence in the approach as well as the models. This framework, in tandem with the 

framework for conducting dependency analyses, is envisaged to provide a means to evaluate how future regulations 

affect operations at a metroplex and how one metroplex compares to another in terms of operational volumes and 

delays.
22

 4.1.2 Influence of aircraft size on delays

Work is underway to analyze the effects of aircraft size on airport delays and determine how to incorporate them 

into the delay-prediction model. Specifically, the analysis will focus on understanding how traffic fleet-mix at an 

airport influences delays. This effort is in the initial stages of research and development to present any significant 

findings in this report.

 4.2 Other studies

In addition to understanding the effects of metroplex dependencies and interactions between airline service networks 

on delays and congestion, the following studies are also being conducted.

 4.2.1 Measures of network efficiency

The trade-studies described earlier employ the number of delayed flights at an airport and/or across NAS as a 

measure of network efficiency/performance. While this serves the purpose of evaluating network performance under 

alternative regimes of airline behavior (as depicted by response of Benevolent Monopolist Airline to variations in 

market conditions), it does not offer deeper insights to answer questions such as: 

a. Is the network robust enough to buffet the spread of delays from one airport to another?

b. Is the network flexible enough to maintain a minimum level of connectivity (in terms of flights and 

passenger throughput) in the event of a critical failure?

c. Which is the more suitable airport in a metroplex to implement a certain regulation or policy?

d. Where should efforts be concentrated to foster the development of the next metroplex such that the current 

problems of delays and congestion are significantly mitigated while enhancing the passenger and flight 

throughput?

e. What is the role of airline competition in influencing answers or solutions to the above questions?

Efforts are underway to identify/define a suitable metric that can not only answer these but is also simple for easy 

computation and analysis.

 4.2.2 Impact of adding capacity

The values in Table 13 depict change in the number of delayed flights (computed using the annual delay-prediction 

model for 2007) when traffic to a metroplex is increased. The results show that an increase in traffic within NYNJ 

led to a larger increase in NAS-wide delays than the same increase at either NorCal or SoCal. On the other hand, the 

increase in delays within each of these metroplexes was almost the same. The changes were computed with respect 

to the delays predicted by the model before any change in traffic in 2007. These results were computed assuming no 

change in airport and metroplex capacity.

Table 13. Impact of change in traffic on delays at a metroplex and across NAS

Metroplex

NAS-wide delays*

Change in NAS-wide delays 

(%) Change in metroplex delays (%)

10% increase 

in traffic 

20% increase 

in traffic 

10% increase 

in traffic

20% increase 

in traffic

10% increase 

in traffic

20% increase 

in traffic

NYNJ 3,341,504 3,368,661 0.79 1.61 10.08 23.01 

NorCal 3,322,755 3,330,218 0.22 0.45 11.20 23.00 

SoCal 3,322,528 3,329,757 0.22 0.44 10.55 24.30 

*Predicted number of NAS-wide delayed flights before change in metroplex traffic = 3,315,321

This case-study was conducted to answer the following questions:

a. How does an increase in capacity impact delays at a metroplex and across NAS?
23

b. Which metroplex is the most appropriate candidate for adding capacity to achieve maximum reduction in 

delays both at the metroplex and across NAS?

To answer these questions, the efficacy of different mechanisms to increase capacity will be investigated. These 

include options such as changing the composition of traffic to a metroplex (fleet-mix) and increasing airport 

capacity. This complements the efforts described earlier to improve the delay models by incorporating factors such 

as aircraft size and fleet mix into them.

 4.2.3 Bayesian-based model

A model using a Bayesian-approach is being developed in parallel with the regression-based model to predict the 

number of delayed flights, to provide an alternate description to what causes delays and how they propagate across 

the NAS. The Bayesian approach offers certain advantages over the regression approach, such as the ability to 

account for uncertainties and noise in the data, and the flexibility to be altered quickly if the set of variables are 

found to be incomplete or inaccurate. The chart in Fig. 19 compares the regression and Bayesian models against data 

for the OEP 35 airports in 2007. Note that the y-axis depicts the number of delayed operations at an airport as a 

percentage of its total annual operations. These initial results showed that as yet there was no clear indication as to 

which approach is more successful at predicting the number of delayed flights. Research is currently underway to 

both improve the Bayesian model and to determine if its advantages clearly out-perform the regression approach.

Fig. 19. Comparison between the regression-based model and the Bayesian-based model
24

Appendix A

Introduction to FAA Circular AC: 150/5060-5

The Circular provides empirical relationships to compute the annual service volumes at an airport for planning 

purposes. As the name suggests, it is only an advisory document providing guidelines to airport planners. The 

circular was released in 1983 using then-existing standards for aircraft separation, air traffic controller workload 

criteria etc. Consequently, the team wanted to validate its applicability to current airport operations. The circular 

provides projections of airport annual service volume (ASV) by means of two parameters – aircraft mix index and 

the runway configuration to be used most.

 A.1 Aircraft mix-index

This parameter is indicative of the type of aircraft that fly into and out of an airport. The FAA identifies four classes 

of aircraft as shown in Table 14. The aircraft mix index is defined as in Eq. (B1), where C and D represent the 

percentage of aircraft belonging to classes C and D, respectively, flying into and out of an airport in a year.

Table 14. Aircraft classification

Aircraft class Max. take-off weight 

(lbs.) No. of engines

A

12,500 or less Single

B Multi

C 12,500 – 300,000 Multi

D Over 300,000 Multi

 A.2 Runway configurations and ASV

The circular provides estimates of ASV based on the particular runway configuration that an airport utilizes 80% of 

the time that produces the maximum hourly capacity, and it prescribes the ASV for the particular configuration as in 

Fig. 20.

Fig. 20. ASV and runway configurations
25

 A.3 Most-used runways and runway configurations – ASPM data

The Circular describes 19 possible runway configurations to estimate the ASV and hourly capacity under both IFR 

and VFR conditions at an airport (Fig. 20). In order to determine the most-used runway configurations at an airport 

the ASPM database was used for 15-minute operations at an airport. This database provides the particular runway 

configuration used at an airport for every 15-minute period in a day (Table 15). It was also deemed necessary to 

determine how each runway was used at an airport. For this, the sample data in was approximated as shown in Table 

16. It was estimated that all runways in a particular configuration were used equally to conduct arrivals or 

departures. For example, runway 4R was used in two configurations for arrivals (Table 15). For the first 

configuration, it shared the arrivals with runway 13R and with 13L in the second. It was estimated that runways 4R 

and 13R were each used to conduct 692 (=1384/2) arrivals for the particular 15-minute period. And, runways 4R and 

13L were each used to conduct 74 (≈147/2) arrivals for the same 15-minute period. On the other hand, only runway 

13L was used for all departures in this 15-minute period. Therefore, 4R and 13R have no departures (Table 16). This 

simple estimation procedure was used to determine the annual arrivals and departures for each runway at an airport.

Table 15. Sample ASPM data for an airport

Runway configurations per 15 minutes Arrivals Departures

4R, 13R | 13L 1384 978

4R, 13L | 13L 147 106

Table 16. Estimate of operations per runway

Runway Total Arrivals Total 

Departures

Total 

Operations

4R 766 -- 766

13R 692 -- 692

13L 74 1084 1158
26

Appendix B

Estimating ASV for LGA and NYNJ

LGA‘s operations were used to validate the Circular, which states that the most-used runway configuration should 

have been used 80% of time to estimate ASV. But LGA has no such runway configuration. As such, each runway‘s 

usage was estimated as described earlier to determine the most-used configuration. For example, in 2007, based on 

the estimation, runways 22 and 31 at LGA were most used for arrivals whereas all runways were used for 

approximately the same amount of time for departures and total operations (Table 17). Therefore the most-used 

configuration for LGA in 2007 was found to be that shown in Fig. 21.

Table 17. Estimates of runway usages at LGA for 2007

Runway Arrivals Departures Total Arrivals 

(%)

Departures 

(%) Total (%)

4 34115 47282 81397 17.78 24.71 21.24

13 3404 86205 89609 1.77 45.05 23.39

22 104652 1973 106625 54.55 1.03 27.83

31 49179 54767 103946 25.63 28.62 27.13

Fig. 21. Runway layout of LGA

Using the Circular, the ASV for LGA‘s most-used configuration in 2007 (Fig. 21) was determined to be 225,000. 

The computed ASV values for LGA from 2002 to 2007 are compared against actual data in Table 18. The T-100 

database maintained by BTS was used to compile this actual operations data. It can be observed that the Circular 

always under-estimated LGA‘s ASV. This can be attributed to the fact that the circular was compiled using the 

operational and safety standards from the 1980s, which have significantly changed over the years. The ASV of 

NYNJ was estimated by treating it as a very large airport with widely separated runways. The team only considered 

EWR, JFK and LGA as the constituent airports of NYNJ and their runways to determine the most-used runway 

configurations (Table 19). Again, the Circular under-estimates for the same reasons described earlier.
27

Table 18. Validation of the FAA circular using LGA’s data

Year Data (T-100) ASV from FAA Circular

2007 377,077 225,000

2006 385,542 225,000

2005 384,906 225,000

2004 385,122 225,000

2003 358,248 225,000

2002 280,575 225,000

Table 19. Estimation of ASV for NYNJ

Year Data (T-100) ASV from FAA Circular

2007 1,233,473 225,000

2006 1,184,445 225,000

2005 1,141,250 225,000

2004 1,113,671 225,000

2003 1,016,652 225,000

2002 913,420 225,000
28

Appendix C

Airline Competition at NYNJ

In addition to the airline redundancy issue in Table 5, further analysis was conducted to gain more insights into 

metroplex operational dependencies. Airline domestic operations were analyzed to find correlations between a lowcost airline (JetBlue) and those of a regional airline (American Eagle) and a legacy airline (US Airways) at the three 

major NYNJ airports from 1995 to 2005 (Fig. 22). 125 markets were used for this study to understand the evolution 

of airline service networks five years before and five years after JetBlue began operations. Note that JetBlue began 

operations in NYNJ airports starting from JFK in 2000 and did not begin operations at the other two airports until 

after 2003. These were the markets that had at least 24 flights to NYNJ in the third quarter of 2007, the period when 

NYNJ experienced some of the worst delays in history. It can be observed that, although American Eagle (AE) and 

US Airways (US) altered their operations at JFK, their overall presence at NYNJ was unaltered by JetBlue‘s 

operations. Note that US Airways discontinued service to JFK in 1999.

Fig. 22. Another example for metroplex operational dependency

It is to be noted that no direct influence of JetBlue on AE‘s and US‘ operations was observed. These airline‘s 

networks were also analyzed to find any correlation in the type of markets they served. The red vertical bars in Fig. 

22 denote the number of markets common to JetBlue and AE, and the purple bars the markets common to JetBlue 

and US (these are listed in the boxes pointing to each of the airports‘ graph). It can be observed that there were very 

few common markets between JetBlue and AE, and between JetBlue and US. Also, the markets common to JetBlue 

and AE were small and a short distance from JFK. Only Boston and Ft. Lauderdale (in Florida) were long distance 

markets served by these airlines. As earlier, no direct correlation between airline operations could be found. One 

likely explanation is that airlines respond to each other‘s operations and business models, and though correlations 

exist, they require a rigorous analysis to be detected.
29

Appendix D

Glossary of Network Theory Terminology

Network Property Equation & Sample Calculation

Degree (k): number of links connected to a node. This 

was denoted as ―deg‖ or ―links‖ in the delay-predictor 

model. If weights are used on links, it is ―dom_traffic‖

kA = 3 for this sample network

Clustering coefficient of a node (Ci): quantifies the 

closeness of nodes in a given network. It can be used to 

determine the ―robustness‖ of the network; it has no 

dimensions.

i

number of trianglesconnected tonodei

number of triplescentered onnodei

C

For the sample network above:

CA = CD = 2/3; CB = CC = 1/1 = 1

Eigen vector centrality of a node (evc): quantifies how 

well a node is connected through its neighbors. It is a 

measure of the importance of a node in network in terms 

of the degree of its neighbors. 

Ax = I, where A is the adjacency matrix 

depicting the presence of links and x is the eigen 

vector of A corresponding to the largest eigen 

value.

For the example network above,

0 1 1 1

1 0 0 1

1 0 0 1

1 1 1 0

A

x = [0.5573 0.4352 0.4352 0.5573], showing 

that nodes A and D are more important to the 

connectivity of the network than B and C.
30

Appendix E

Glossary

Organizations and Architectures

ATL Hartsfield-Jackson Atlanta International Airport

ATC Air Traffic Control

BTS Bureau of Transportation Statistics

DEN Denver International Airport

FAA Federal Aviation Administration

GMU George Mason University

ICAO International Civil Aviation Organization

NAS National Airspace System

PANYNJ Port Authority of New York & New Jersey

SoS System-of-systems

TRACON Terminal Radar Approach Control

WTO World Trade Organization

Metroplex Airport (code)

New York/New Jersey 

(NYNJ)

Newark Liberty International Airport (EWR)

John F. Kennedy International Airport (JFK)

La Guardia Airport (LGA)

Northern California

(NorCal)

San Francisco International Airport (SFO)

Metropolitan Oakland International Airport (OAK)

Norman Y. Mineta San Jose International Airport (SJC)

Southern California 

(SoCal)

Los Angeles International Airport (LAX)

Long Beach Airport—Daugherty Field (LGB)

Ontario International Airport (ONT)

John Wayne-Orange County Airport (SNA)

Chicago

Chicago O'Hare International Airport (ORD)

Chicago Midway International Airport (MDW)

General Mitchell International Airport, Milwaukee, WI (MKE)

Southern Florida

Miami International Airport (MIA)

Palm Beach International Airport, West Palm Beach, FL, (PBI)

Fort Lauderdale/Hollywood International Airport (FLL)

Washington, DC

Ronald Reagan Washington National Airport (DCA)

Washington Dulles International Airport (IAD)

Baltimore/Washington International Thurgood Marshall Airport (BWI)
31

Datasets

ASPM Aviation System Performance Metrics

LMINET 102 Set of 102 airports used in a queuing network model of the National Airspace System 

developed by the Logistics Management Institute (LMI)

OEP 35 Data maintained by the FAA on 35 airports as part of the Operational Evolution Partnership 

program

OPSNET 45 Data maintained by the FAA about operations at 45 airports

T-100 Database maintained by the BTS and includes non-stop segment and on-flight market data

Modeling parameters

arr arrivals

ASV Annual Service Volume of an airport

Avg. Clust. Coeff. Average Clustering Coefficient of a network

BMA Benevolent Monopolist Airline

CC Clustering Coefficient of a node in network

di,j distance between centers of rwyi and RR

dl(i) length dependency of rwyi

do(i) orientation dependency of rwyi

dp(i) proximity dependency of rwyi

di,j distance between rwyi and rwyj

dep departures

IFR Instrument Flight Rules

lilength of rwyi

lRR length of RR

RDM Runway Dependency Metric

RDMi RDM for rwyi

RDM-A RDM for an airport

RMD-M RDM for a metroplex

RR Reference Runway

rwy runway

rwyi runway i at an airport or a metroplex

Rwy_opsi number of flights operated from rwyi

VFR Visual Flight Rules
32

References

1. Maier, M. W., ―Architecting Principles for Systems-of-Systems,‖ Systems Engineering, Vol. 1, No. 4, 1998, pp. 

267–284.

2. DeLaurentis, D., ―Understanding Transportation as a System-of-Systems Design Problem,‖ 43rd AIAA 

Aerospace Sciences Meeting and Exhibit, Reno, NV, AIAA Paper 2005-123, Jan. 2005. 

3. DeLaurentis, D. A., and Callaway, R. K., ―A System-of-systems Perspective for Public Policy Decisions,‖ 

Review of Policy Research, Vol. 21, No. 6, Nov. 2004, pp. 829–837.

4. Ayyalasomayajula, S., Fry. D. N., and DeLaurentis, D. A., ―A Framework for Formulating Design Problems in 

a System-of-Systems Context,‖ 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 

Victoria, British Columbia, Canada, AIAA Paper 2008-5818, Sep. 2008.

5. ―The Port Authority of New York & New Jersey,‖ http://www.panynj.com [retrieved 20 December 2008].

6. Federal Aviation Administration, ―Aviation System Performance Metrics (ASPM) – Complete,‖ [online 

database], http://aspm.faa.gov/main [retrieved 20 December 2008].

7. Newman, M. E. J., ―The structure and Function of Complex Networks,‖ SIAM Review, Vol. 45, 2003, pp. 167–

256.

8. Barabási, A., ―Linked: The New Science of Networks,‖ Perseus Book Group, Cambridge, MA, 2002.

9. DeLaurentis, D., Han, E., and Kotegawa, T., ―Network-theoretic Approach for Analyzing Connectivity in Air 

Transportation Networks,‖ Journal of Aircraft, Vol. 45, No. 4, 2008, pp. 1669–1679.

10. Bureau of Transportation Statistics, ―Database Name: Air Carrier Statistics (Form 41 Traffic) - U.S. Carriers,‖ 

[online database], 

http://www.transtats.bts.gov/Tables.asp?DB_ID=110&DB_Name=Air%20Carrier%20Statistics%20(Form%20

41%20Traffic)-%20%20U.S.%20Carriers&DB_Short_Name=Air%20Carriers [retrieved 10 February 2008].

11. U.S. Census Bureau, ―Census 2000 Gateway,‖ [online database], 

http://www.census.gov/main/www/cen2000.html [retrieved 15 April 2008].

12. Wang, L., Donohue, G., Hoffman, K., Sherry, L., and Oseguera-Lohr, R., ―Analysis of Air Transportation for 

the New York Metroplex: Summer 2007,‖ 3rd International Conference on Research in Air Transportation –

ICRAT 2008, Fairfax, VA, Jun. 2008.

13. Federal Aviation Administration, ―Advisory Circular: Airport Capacity and Delay,‖ 

http://rgl.faa.gov/Regulatory_and_Guidance_Library%5CrgAdvisoryCircular.nsf/0/641E65B7EA1DC3B68525

6D0C006289F6?OpenDocument [retrieved April 21 2009].
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Analysis of Dependencies and Impacts of Metroplex Operations 

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This report documents research performed by Purdue University under subcontract to the George Mason University (GMU) 

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of the larger efforts led by GMU: a) a literature review on metroplex operations followed by identification and analysis of 

metroplex dependencies, and b) the analysis of impacts of metroplex operations on the larger U.S. domestic airline service 

network. The tasks are linked in that the ultimate goal is an understanding of the role of dependencies among airports in a 

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