September 2015

NASA/TM–2015-218798 

Benefit Assessment for Metroplex Tactical 

Runway Configuration Management (mTRCM) 

in a Simulated Environment 

Nipa Phojanamongkolkij 

Langley Research Center, Hampton, Virginia 

Rosa M. Oseguera-Lohr 

Langley Research Center, Hampton, Virginia 

Gary W. Lohr 

Langley Research Center, Hampton, Virginia 

Steven W. Robbins 

Stinger Ghaffarian Technologies, Inc., Hampton, Virginia

James W. Fenbert and Christopher L. Hartman

Analytical Mechanics Associates, Inc., Hampton, Virginia
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September 2015

NASA/TM–2015-218798 

Benefit Assessment for Metroplex Tactical 

Runway Configuration Management (mTRCM) 

in a Simulated Environment 

Nipa Phojanamongkolkij 

Langley Research Center, Hampton, Virginia 

Rosa M. Oseguera-Lohr 

Langley Research Center, Hampton, Virginia 

Gary W. Lohr 

Langley Research Center, Hampton, Virginia 

Steven W. Robbins 

Stinger Ghaffarian Technologies, Inc., Hampton, Virginia 

James W. Fenbert and Christopher L. Hartman 

Analytical Mechanics Associates, Inc., Hampton, Virginia 
Available from: 

NASA STI Program / Mail Stop 148 

NASA Langley Research Center 

Hampton, VA 23681-2199 

Fax: 757-864-6500 

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. 
Table of Contents 

Acronyms ...................................................................................................................................................................... 2

1. Introduction ........................................................................................................................................................... 2

2. Description of SORM and the metroplex TRCM algorithm ................................................................................. 3

2.1. Metroplex TRCM algorithm delivered by Mosaic ATM (mTRCM-A)....................................................... 4

2.2. Metroplex TRCM algorithm developed by LaRC (mTRCM-B).................................................................. 4

2.3. Comparative assessments of the metroplex TRCM algorithms ................................................................... 5

3. Method of test ....................................................................................................................................................... 6

3.1. Test cases ............................................................................................................................................................ 6

3.2. Metrics definitions .............................................................................................................................................. 9

3.3. Data collection ..................................................................................................................................................10

3.4. Analysis technique ............................................................................................................................................10

4. Analysis Results ..................................................................................................................................................10

5. Conclusions and future research .........................................................................................................................27

Reference .....................................................................................................................................................................28

1
Acronyms 

AAR = Airport Acceptance Rate 

ADR = Airport Departure Rate 

ASPM = Aviation System Performance Metrics 

EWR = Newark Liberty International airport 

FAA = Federal Aviation Administration 

JFK = John F. Kennedy International airport 

JPDO = Joint Planning and Development Office 

LGA = LaGuardia airport 

MILP = Mixed Integer Linear Programming 

MSE = Metroplex Simulation Environment 

mTRCM = Metroplex Tactical Runway Configuration Management 

N90 = New York Terminal Radar Approach Control, or TRACON 

NAS = National Airspace System 

NextGen = Next Generation Air Transportation System 

SD = Standard deviation 

SME = Subject matter expert 

SORM = System-Oriented Runway Management 

TE = Throughput efficiency 

TEB = Teterboro airport 

TRCM = Tactical Runway Configuration Management 

1. Introduction 

The System-Oriented Runway Management (SORM) concept is a collection of capabilities focused on ensuring 

efficient use of runways while considering all of the factors that affect runway use. Examples of these factors are: 

surface wind velocity, meteorological conditions, traffic demand, adjacent airport traffic flows, severe weather 

activity, environmental factors, intersecting arrival/departure runways, distance between runways, etc. This myriad of 

factors requires consideration in arriving at runway configurations that collectively best serve the system as a whole. 

Tactical Runway Configuration Management (TRCM), is one of the SORM capabilities; a software tool to carry out 

this function was developed by Mosaic ATM (contract number NNL09AA02B), and has two separate and distinct 

algorithms. One is specifically tailored to a single airport environment, where there is no inter-relationship between 

the studied airport and other proximate airports; this single-airport TRCM tool was used in a previous study, to assess 

the benefits at selected airports [Refs. 1, 2]. The other TRCM algorithm was developed for a metroplex environment1

. 

The metroplex environment poses considerable challenges to TRCM. The selection of runway configurations across 

airports within a given metroplex area requires consideration of the role of each airport in the grander context of 

National Airspace System (NAS) efficiency, as well as user preferences where possible. The focus of this report is 

limited to the metroplex environment and will be referred to as Metroplex TRCM or mTRCM. 

A preliminary evaluation of the mTRCM algorithms was conducted for the New York metroplex (N90) 

environment, using two six-hour traffic scenarios in August 2011 [Ref. 3)]. There were four airports considered as 

part of the N90 (New York Terminal Radar Approach Control, or TRACON) metroplex: John F. Kennedy 

International Airport (JFK), LaGuardia Airport (LGA), Newark Liberty International Airport (EWR), and Teterboro 

Airport (TEB). The initial results documented benefits on transit time and total delay using mTRCM at this metroplex. 

This report focuses on a benefit assessment of total delay, transit time, and throughput efficiency benefits using 

the metroplex TRCM algorithm at representative volumes for today’s traffic at the New York metroplex. The New 

York metroplex was selected for the complexity in both airport configuration decisions and runway assignment and 

sequencing decisions. Any insight on potential algorithm improvement from this assessment will be recommended 

for future research. The paper is organized as follows: Section 2 gives a description of SORM and the metroplex 

TRCM algorithms. Following this description of the algorithms, method of test is presented in section 3. Section 4 

provides the results of this assessment. Conclusions and proposed future research are detailed in section 5. 

 

1

 The JPDO defines a metroplex as “a group of two or more adjacent airports whose arrival and departure operations 

are highly interdependent.” [Ref. 12] 

2
2. Description of SORM and the metroplex TRCM algorithm 

A thorough description of the SORM software can be found in Mosaic ATM [Ref. 4]. Figure 1 gives an overview 

of how the SORM software operates. The current software requires three types of inputs, which are weather 

information, airport information, and flight plans. Weather information includes wind speed and wind direction. 

Airport information includes layout of terminals, spots2

, runways, arrival fixes, departure fixes, and their associated 

latitude and longitude coordinates, available runways, and runway policy (dedicated arrivals/departures or mixed-use 

operation). Flight information includes flight ID, origin and destination airports, aircraft type, assigned arrival or 

departure fix, assigned spot, scheduled arrival time at fix (arrivals), and scheduled gate departure time (departures). 

The SORM Manager (Figure 1) alternates the control between planning and execution cycles. During the planning 

cycle (labeled as Planning in TRCM in the figure), it uses the metroplex TRCM algorithm (detailed in sections 2.1 

and 2.2) for the next 90-minute simulation time segment. In the current algorithm implementaion, a runway 

configuration change is not allowed during the freeze interval (the first 45-minute interval). The metroplex TRCM 

algorithm produces a plan listing recommended runway configurations and their associated change schedules during 

the planning interval (the second 45-minute interval). If the algorithm cannot find a better configuration than the 

current configuration, then the plan will be an empty list. 

After each iteration of the planning cycle, the SORM software then operates in the execution cycle (Execution in 

Metroplex Simulation Environment (MSE)) simulating the airborne and taxi routes for individual aircraft using a 

medium-high fidelity simulation environment, called the MSE. It uses the airport information and flight information 

along with the runway configuration determined in the most recent planning cycle as inputs to simulate the arrival and 

departure operations. The MSE includes sufficient level of detail for airport surface operations using node-link3

connections replicating the actual airport surface layout. It also incorporates aircraft-specific4 airborne-speed and taxispeed profiles. The MSE records several flights’ status, including all simulated flight routes and their time stamps at 

all predetermined surface nodes. It provides the latest status to the planning cycle for its next iteration. These 

alternating cycles continue until all flights under study are simulated. Any performance metrics can be post-processed 

and computed from the MSE flight records. 

Figure 1. Overview of the SORM software. 

 

2

 Spot is the position where hand-off responsibility transfers between non-movement area (or ramp) and movement 

area controllers. 

3

 Node-link connections represent a system of interconnected small surface segments along which an aircraft can 

travel. For modeling purpose, stopping and holding are allowed at nodes (endpoints of segments). Continuous 

movement is only allowed on links. 

4

 Aircraft-specific refers to different aircraft make and model (e.g., A318,B747, DC8, etc.) 

3
2.1. Metroplex TRCM algorithm delivered by Mosaic ATM (mTRCM-A) 

The metroplex TRCM algorithm provided by Mosaic ATM uses Mixed Integer Linear Programming (MILP) to 

solve each airport independently for the airport-specific optimal runway configurations. Because each airport is solved 

independently, there is no constraint on interrelated metroplex conflicts captured in the current algorithm. Within each 

airport, the MILP requires a pre-processing step for flight input. All flights during the planning period are first 

sequenced according to a first-in-first-out (FIFO) rule. Next, appropriate runway separation rules between (pairwise) 

consecutive arrivals and departures are enforced, generating the list of flights ordered by their earliest time at runways 

under all possible runway assignments and routing policies. The MILP only considers flights from runway to fix 

(departures) or from fix to runway (arrivals), and does not capture any surface operations. Constraints of the MILP 

generally include (1) allocation of exactly one active runway configuration at any given time; (2) runway assignment 

of each flight exactly once to the active configuration; (3) enforcement of the FIFO rule on each active runway; and 

(4) no more than one configuration change during the planning period. The objective function to be minimized by the 

MILP includes (1) the cost of changes (in unit of time) from one active configuration to another; and (2) the cost of 

using runways (in time) for all flights. Wind speed and direction are captured in the objective function as a higher cost 

of using affected runways by affected flights. Currently this algorithm allows each airport within the metroplex to 

have different change times (i.e., relaxing the necessary coordination). Change times are only allowed in increments 

of 15 minutes. The first change time allowed is at 45 minutes from the start time because of the freeze time horizon 

(thus changes could take place at time 45 minutes, 60, 90, etc., from the start time. 

2.2. Metroplex TRCM algorithm developed by LaRC (mTRCM-B) 

The metroplex TRCM algorithm developed by LaRC (mTRCM-B) uses two sequential enumerative searches. 

Figure 2 shows the conceptual depiction of the algorithm. The first search is performed for all airports within the 

metroplex and is equivalent to Mosaic’s single airport TRCM. During the first enumerative search, the low fidelity 

simulation environment is used to calculate the total transit time5

 (the objective value) of all flights at each airport 

independently. The search trades off the airborne, taxi, and queue (departing aircraft only) time durations for the 

minimum total time. The low fidelity simulation computes transit time durations using the great circle distances for 

all airborne and taxi routes, along with their corresponding speeds. Wind speed and direction are taken into account 

to penalize runway configurations that should not be used during unfavorable wind conditions. This is accomplished 

by adding a penalty to the total transit time. At the end of this first enumerative search, airport-specific lists (one list 

for each airport) detailing the optimum change times and objective values of all available configurations are given as 

inputs to the second enumerative search. The airport-specific lists are shown in figure 2 as “Best Airport 1 list”, ..., 

“Best Airport N list”. 

The second search generates all feasible combinations of the airport-specific lists, which is referred to as all 

feasible metroplex configurations. The search loops through these metroplex configurations while ensuring each 

configuration is not on the excluded metroplex configuration list. This list, provided by the user, contains combinations 

of configurations that exhibit interrelated metroplex conflicts that are not operationally allowed. The search at any 

given time only keeps track of two configurations, the best found and the contender. If the first configuration is not 

on the excluded list, it will become the best found. The next configuration on the list becomes the contender. If the 

contender is on the excluded list, it is rejected and the next configuration on the feasible list becomes the contender. 

At any time during the search, if the contender outperforms the best found (in terms of the total metroplex transit time 

of all flights), then the search will replace the best found with the contender. The evaluation repeats until all feasible 

metroplex configurations are considered. At the end of the search, the best found is the optimum metroplex 

configuration. The optimum metroplex configuration provides the optimum runway configurations at all airports along 

with their optimum configuration change times (shown in figure 2 as “The Optimum Metroplex Configuration”). 

Currently this algorithm allows each airport within the metroplex to have different change times (i.e., relaxing the 

necessary coordination). Change times are allowed in increments of 5 minutes (e.g., at time 45 minutes, 50, 55, etc., 

from the start time.) 

 

5

 Transit time is defined as the travel time from arrival fix to spot (for arriving aircraft), or from spot to departure fix 

(for departing aircraft). 

4
Figure 2. Overview of the metroplex TRCM algorithm by the LaRC researchers. 

2.3. Comparative assessments of the metroplex TRCM algorithms 

Table 1 compares the two metroplex TRCM algorithms described in sections 2.1 and 2.2. The different algorithms 

are used only in the Planning in TRCM process segment. The same simulation (MSE) is used in both algorithms for 

the Execution in MSE. 

Table 1. The metroplex TRCM algorithm comparison. 

Features Metroplex TRCM provided

by Mosaic ATM (mTRCMA)

Metroplex TRCM developed at 

LaRC (mTRCM-B)

Algorithm

elements

Planning in TRCM MILP solver capturing travel 

duration between runways 

and fixes, or in other words 

only airborne duration

Two sequential enumerative 

searches for the optimum combined 

airborne and surface durations

(between spots and fixes) using 

low fidelity fast-time simulation

Execution in MSE MSE

The two metroplex TRCM (mTRCM) algorithms (mTRCM-A and mTRCM-B) were evaluated based on transit 

time [Ref. 5]. Using two six-hour test days in August 2011, the mTRCM-A provided transit time savings between 43 

and 114 seconds per arrival for the metroplex; and a penalty of 79 seconds and a saving of 71 seconds per departure 

for the metroplex . In comparison, the mTRCM-B provided transit time savings between 16 and 104 seconds per 

arrival for the metroplex, and a saving between 16 and 95 seconds per departure for the metroplex. 

Overall, the mTRCM-B algorithm gives consistently better average transit time savings per flight over all airports 

within the metroplex than the mTRCM-A. Also, because the mTRCM-B implementation allows the exclusion of 

configuration combinations that are known to be infeasible in normal metroplex operations, it is more realistic . In 

addition, the mTRCM-B algorithm uses the single-airport TRCM that was previously evaluated at JFK and two 

additional airports outside the N90 metroplex [Refs. 1, 2], so the evaluation team was very familiar with its operation. 

Since the team did not have as much confidence in the results obtained from the mTRCM-A algorithm, it was decided 

that this algorithm would not be used for the benefits assessments. Since only the mTRCM-B algorithm will be used, 

for the remainder of this analysis and will be referred to as the mTRCM algorithm. 

Airport 1

Enumerative Search

Best 

Airport 1

list

Metroplex

Enumerative Search

List of 

forbidden 

metroplex 

config.

Airport 2

Enumerative Search

Airport N

Enumerative Search ...

Best 

Airport 2

list

Best 

Airport N 

list

The Optimum 

Metroplex

Configuration

text

text

First Enumerative Search

Second Enumerative Search

5
3. Method of test 

3.1. Test cases 

For this assessment, a set of historical data that covers a period from late 2009 to 2010 was used. The selected days 

for testing are a set of twelve days identified by the Federal Aviation Administration (FAA) and the Joint Planning 

and Development Office (JPDO) for NextGen research. These days were identified by the FAA as capturing seasonal 

trends in the National Airspace System (NAS) for that year, using an optimization based method to minimize 

differences in predicted and actual levels of desired performance metrics at the daily level [Ref. 6]. The New York 

metroplex (N90) was used for this assessment of the mTRCM algorithm because it has been identified (along with 

another 6 metroplex sites) as a prioritized metroplex site for FAA’s implementation of key NextGen operational 

improvement areas through 2018 [Ref. 7]. The four airports considered in this metroplex are JFK, LGA, EWR, and 

TEB. 

Figure 3 shows relative airport locations for the four N90 airports. JFK has two sets of perpendicular parallel 

runways. One is in a southeast/northwest orientation (13R/31L and 13L/31R). The other is in a northeast/southwest 

orientation (4L/22R and 4R/22L). LGA has intersecting runways; one is in a southeast/northwest orientation (13/31) 

and the other is in a northeast/southwest orientation (4/22). EWR has a set of parallel runways in a northeast/southwest 

orientation (4L/22R and 4R/22L) intersecting with a runway in a southeast/northwest orientation (11/29). TEB has 

intersecting runways; one is in a north/south orientation (1/19) and the other is in a northeast/southwest orientation 

(6/24). 

Figure 3. Relative airport locations for JFK, LGA, EWR, and TEB, image courtesy of Google Earth.

Runway configurations evaluated in this assessment are based on the most frequently used from the historical data. 

Table 2 gives a complete list of all evaluated configurations in this assessment. There were 14 runway configurations 

evaluated at JFK, 10 configurations at LGA, 10 configurations at EWR, and 6 configurations at TEB. A runway 

configuration in this report was defined as a set of unique active runways used for arrivals and departures. It did not 

4L, R

JFK

13R, L 22R, L

31L, R

EWR

LGA

TEB

4

31

13 22

4L, R

22R, L

11 29

1

19

6

24

N

6
depend on traffic flow direction or on runway policy (dedicated arrivals/departures or mixed-use operation). At JFK, 

for example, configuration numbers 6 (A_22L_D_22R) and 9 (A_22L_22R_D_22R) are two unique sets of active 

runways although they both operate in the same traffic flow direction with runway 22R operating as a dedicated 

departure in configuration number 6, and as a mixed-use runway in configuration number 9. 

Table 2. Runway configurations. 

(A_rwy1_D_rwy2_rwy3 = Arrivals primary on runway 1; Departures on runways 2 or 3) 

Configuration 

Number

JFK LGA EWR TEB

1 A_31R_31L_D_31L A_31_D_4 A_22L_D_22R A_19_D_24

2 A_22L_D_22R_31L A_22_D_31 A_4R_D_4L A_6_D_1

3 A_13L_22L_D_13R A_22_D_13 A_22L_11_D_22R A_1_D_1

4 A_31R_D_31L A_4_D_13 A_4R_11_D_4L A_19_D_19

5 A_4R_D_4L_31L A_31_D_31 A_22L_D_22R_29 A_24_D_24

6 A_22L_D_22R A_4_D_4 A_4R_4L_D_4L A_6_D_6

7 A_13L_D_13R A_13_D_13 A_4R_29_D_4L

8 A_4R_D_4L A_22_D_22 A_4L_D_4L

9 A_22L_22R_D_22R A_4_D_31 A_29_D_29

10 A_4R_4L_D_4L_31L A_13_D_4 A_11_D11

11 A_4R_4L_D_4L

12 A_22L_22R_D_22R_31L

13 A_13L_D_13R_13L

14 A_22L_22R_D_31L

As an example of several interrelated N90 metroplex conflicts, departures from runway 22 at LGA are likely to 

interfere with traffic to and from other airports (see figure 3). As such, this departure runway configuration should be 

avoided. If runway 13/31 is closed or winds requires the use of runway 22, then runway 22 will be used for both 

arrivals and departures (i.e., A_22_D_22 in table 2). Other examples of interrelated N90 conflicts can be found in Ref. 

11. In the current algorithm, a list of excluded metroplex configurations has been captured. Table 3 gives details of 

this list. The first column is the operational constraints, which have been modeled in the algorithm according to the 

last two columns. 

7
Table 3. List of excluded metroplex configurations implemented in the mTRCM algorithm. 

Operational constraints Modeling constraints

When LGA is in JFK is not allowed to be in

When LGA lands on Runway 4, and JFK is 

on ILS to Runways 22L|22R6, LGA cannot 

depart on Runway 13.

A_4_D_13 (Conf #4) A_22L_D_22R_31L (Conf #2)

A_13L_22L_D_13R (Conf #3)

A_22L_D_22R (Conf #6)

A_22L_22R_D_22R (Conf #9)

A_22L_22R_D_22R_31L (Conf #12)

A_22L_22R_D_31L (Conf #14)

Instrument approaches (LOC) to LGA 31 

must fly a loop which uses JFK airspace

areas, requiring JFK to use a 31L|31R for 

arrival configuration.

A_31_D_4 (Conf #1) A_22L_D_22R_31L (Conf #2)

A_13L_22L_D_13R (Conf #3)

A_4R_D_4L_31L (Conf #5)

A_22L_D_22R (Conf #6)

A_13L_D_13R (Conf #7)

A_4R_D_4L (Conf #8)

A_22L_22R_D_22R (Conf #9)

A_4R_4L_D_4L_31L (Conf #10)

A_4R_4L_D_4L (Conf #11)

A_22L_22R_D_22R_31L(Conf #12)

A_13L_D_13R_13L (Conf #13)

A_22L_22R_D_31L (Conf #14)

A_31_D_31 (Conf #5) A_22L_D_22R_31L (Conf #2)

A_13L_22L_D_13R (Conf #3)

A_4R_D_4L_31L (Conf #5)

A_22L_D_22R (Conf #6)

A_13L_D_13R (Conf #7)

A_4R_D_4L (Conf #8)

A_22L_22R_D_22R (Conf #9)

A_4R_4L_D_4L_31L (Conf #10)

A_4R_4L_D_4L (Conf #11)

A_22L_22R_D_22R_31L(Conf #12)

A_13L_D_13R_13L (Conf #13)

A_22L_22R_D_31L (Conf #14)

Table 4 provides details of the twelve days used for analysis. These days are representative of the 2009-2010 time 

period and cover a range of weather conditions and traffic volumes. The traffic volumes did not include military 

flights. There were 4 weekend days. JFK, LGA, and EWR had approximately the same traffic volumes (~500 arrivals 

and 500 departures per day) for weekdays. Weekend and weekday volumes did not significantly differ at JFK and 

EWR. However, the weekend volumes at LGA differed significantly from the weekday volumes. The majority of TEB 

traffic was general aviation, and rather constant across all test days in this assessment. 

 

 

6

 Arrival | Departure 

8
Table 4. Test cases. 

(A = Arrival Operations, D = Departure Operations) 

Test 

#

Date (Day of 

the week)

N90 JFK LGA EWR TEB

#A #D #A #D #A #D #A #D #A #D

1 20091006 (Tue) 1,567 1,610 428 479 502 506 524 530 113 95

2 20091017 (Sat) 1,237 1,297 449 501 269 269 406 433 113 94

3 20091120 (Fri) 1,787 1,769 527 544 564 560 582 570 115 95

4 20100110 (Sun) 1,574 1,546 507 520 455 447 498 485 115 94

5 20100309 (Tue) 1,754 1,731 503 515 553 544 580 577 118 95

6 20100325 (Thu) 1,792 1,819 526 555 567 564 584 604 115 96

7 20100506 (Thu) 1,763 1,794 491 536 582 582 574 583 116 93

8 20100518 (Tue) 1,706 1,739 484 515 564 564 544 562 114 98

9 20100605 (Sat) 1,306 1,368 480 528 288 290 426 455 113 95

10 20100703 (Sat) 1,407 1,436 524 561 305 300 467 481 112 94

11 20100713 (Tue) 1,541 1,532 494 519 426 403 508 515 114 95

12 20100722 (Thu) 1,835 1,863 565 592 565 567 592 608 114 96

3.2. Metrics definitions 

The primary metrics for this assessment were average (per flight) total delay and throughput. Total delay is the 

sum of delays within metroplex airspace (airborne between fix and runway), delay on the ground (surface), and delay 

outside the arrival fix (arrival only). Delay is the difference between actual and ideal time durations. The ideal time 

duration is the un-delayed shortest time through the metroplex regardless of weather conditions if an aircraft is the 

only aircraft in the system at the time it is operated. Delay within metroplex airspace is the difference between actual 

flying (airborne) and ideal flying time durations. Delay on the ground is the difference between actual surface (taxi 

plus any departure queue times) and ideal surface time durations. The ideal surface time duration of any departure 

does not include any departure queue time duration because it is the only departure in the system, and therefore it does 

not need to be in the queue. Delay outside fix is for arrival only and is the difference between actual time stamp7

 and 

scheduled time stamp8

 at the arrival fix. 

Theoretically, there is a relationship between throughput and transit time [Refs. 8, 9]. As throughput increases, 

transit time increases. As throughput approaches the system capacity, transit time increases exponentially, since 

aircraft will be subject to holding. For arrivals, transit time is defined as the travel time from arrival fix to spot. 

Specifically it was computed as the difference between the actual time stamp at spot and the actual time stamp at the 

arrival fix. For departures, it is from spot to departure fix. Specifically, it was computed as the difference between the 

actual time stamp at departure fix and the actual time stamp at spot. Throughput and transit time were directly related. 

That is, if the proposed system has a smaller transit time than the baseline, then it also has a better throughput than the 

baseline. 

Another throughput metric in this analysis is throughput efficiency (TE). TE is the ratio of the actual to the 

optimum effective throughput, and is unit-less. The actual effective throughput is defined as the ratio of the number 

of aircraft that have transited through its exit system point to the total actual transit time of these aircraft, as defined 

in the previous paragraph. The optimum effective throughput is defined similarly, with the exception of using the total 

minimum times instead of the total actual times. The minimum transit times for all fix-spot pairs were determined 

first, and were then used to compute the total minimum times. Any system with TE closer to 1 is preferred. TE has 

been shown to be insensitive to traffic volume in Ref. 10, and, therefore, is appropriate for this assessment when traffic 

volume was the same in the baseline and the proposed systems. As a result, the system with a better throughput will 

be the system with a higher TE. 

 

7

 The actual time stamp is the time stamp of an aircraft obtained from the MSE. 

8

 The scheduled time stamp is the time stamp of an aircraft scheduled in the flight plan. 

9
3.3. Data collection 

To assess the mTRCM benefit, the metrics defined in Section 3.2 must be compared with the actual historical 

operations. Because the traffic volumes did not include military flights, which caused the traffic volumes to be lower 

than those in the FAA Aviation System Performance Metrics (ASPM) database, the metrics of interest were not 

available from the ASPM for a fair comparison. Also, comparing results from two sets of simulated runs (using 

historical vs. mTRCM configuration schedules) was considered to be better for benefits assessment than comparing 

simulated and actual data. This is because all of the operating conditions (e.g., actual flights, standard taxi routes, etc.) 

were controlled in the same way within the simulation environment for both the historical and the mTRCM 

configuration schedules. Therefore, the benefit quantification is considered fair and straight forward. In contrast, the 

benefit quantification by comparing the simulated and actual data becomes complex. The actual operating conditions 

may be different and usually random than those in the simulation environment. Therefore, the benefit could be either 

from them TRCM schedules solely or from the compounding effects of the mTRCM schedules and random operating 

conditions. 

Each test case in Table 4 was run once using the SORM software with the historical runway configurations 

obtained from the ASPM database (i.e., the baseline), and once with the mTRCM-generated runway configuration 

schedule. Specifically, there were a total of 24 runs (12 days × 2 runway schedule cases). For the baseline case, the 

software bypasses the planning cycles (the Planning in TRCM in Figure 1) and only invokes the MSE (the Execution 

in MSE) to simulate the airborne and ground routes of all aircraft given the historical runway configuration schedule. 

The historical runway configuration schedules (the baseline) can be viewed as the schedules produced by subject 

matter experts. The differences between the baseline’s and the mTRCM’s total delay and transit time were computed 

on a flight-by-flight basis. The average (per flight) benefits were then computed for all twelve test cases. Positive 

values indicated there was a benefit of using the mTRCM over the baseline. Negative values indicated a penalty of 

using mTRCM over the baseline. In contrast, TE was computed for both the baseline and mTRCM cases on a system 

level basis (i.e., not for individual flights), and compared for this assessment. Throughput benefit of using the mTRCM 

occurs if its TE is higher than the baseline’s TE, and vice versa. 

3.4. Analysis technique 

 To test if the mTRCM runway schedules outperform the baseline schedules, the well-known t-test is used [Ref. 

13]. However, the t-test assumption is that both schedules produce Normal-distribution metrics. Deviation from this 

assumed distribution requires the use of an equivalent non-parametric Mann-Whitney test (or sometimes called the 

Wilcoxon rank sum test) [Ref. 13]. In this assessment, the Mann-Whitney test is used to determine if the medians, as 

opposed to the mean, of the mTRCM’ s metrics are better than the baseline’s. 

4. Analysis Results 

4.1. Total delay 

 Figure 4 shows per flight average total delay benefits for arrivals and departures for all twelve days at the N90 

metroplex. In the same figure, numbers of arrivals and departures were plotted to determine if any correlation existed 

between demand volume and magnitude of benefit. There was no strong correlation observed. This may be because 

the demand volume is not high enough to observe the strong correlation. Generally, weekend volumes were smaller 

than weekdays. Total delay benefit varied by day. The mTRCM usually provided total delay benefit over the baseline 

for arrivals and departures (ten out of twelve days). Eight out of twelve days total delay benefits for arrivals were 

higher than those for departures. 

 

10
Figure 4. Per flight average delay benefit (in seconds) for arrivals and departures for all days (in Day# - 

mmddyy (day) format) at N90 metroplex. 

 

It is important to note that, because the benefit is considered at the system level (i.e., for the metroplex and not for 

individual airports), some airports might sustain a greater penalty, if it results in a net benefit for the metroplex as a 

whole. Airports within the N90 metroplex were analyzed next. Similar to figure 4, figure 5 shows per flight average 

total delay benefit for arrivals and departures for all twelve days at JFK, LGA, EWR, and TEB. Similarly, there was 

no strong correlation between demand volume and magnitude of benefit observed. Generally, weekend volumes were 

smaller than weekday’s especially at LGA. In contrast, JFK and TEB did not have this trend, while EWR had a slight 

trend. 

 For JFK, the mTRCM reduced total delay compared to the baseline for arrivals and departures. Half of the test 

days had greater total delay reductions for arrivals compared to those from departures, while the opposite was true for 

the other half of the test days. 

 For LGA, the mTRCM provided mixed results of total delay benefits as compared to the baseline. Arrivals had 

negative values (representing a penalty for using the mTRCM over the baseline), while departures had positive values. 

Eleven out of twelve days had total delay benefits for departures higher than those from arrivals. 

 For EWR, all twelve days the mTRCM provided the total delay benefits over the baseline for arrivals and 

departures. All test days showed total delay benefits for arrivals that were higher than those from departures. 

 For TEB, the mTRCM provided net total delay benefits over the baseline for arrivals and departures. All test days 

displayed total delay benefits for arrivals that were higher than those from departures. 

 

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11 - 071310 (T)

12 - 072210 (TH) # Aircraft Benefit per Flight (seconds)

N90

Total Delay - Arrivals Total Delay - Departures #Arrivals #Departures

11
Figure 5. Per flight average delay benefit (in seconds) for arrivals and departures for all days (in Day# - mmddyy (day) format) at all airports within 

N90 metroplex, all plots use a common scale. 

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Total Delay - Arrivals Total Delay - Departures #Arrivals #Departures

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Total Delay - Arrivals Total Delay - Departures #Arrivals #Departures

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Total Delay - Arrivals Total Delay - Departures #Arrivals #Departures

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TEB

Total Delay - Arrivals Total Delay - Departures #Arrivals #Departures

12
 Reductions in total delay (per flight) for all days were computed. These reductions were then averaged over twelve 

days and are shown in figure 6. Overall, the N90 metroplex showed 71 seconds per flight reduction of delay (or 7% 

with respect to the baseline) for arrivals, and 54 seconds per flight reduction (or 5%) for departures. JFK and LGA 

displayed higher percent reductions in departures than arrivals. In contrast, EWR and TEB provided higher percent 

reductions in arrivals than departures. It is noted that LGA had mixed results with a negative reduction (or, an 

equivalent to an increase) in total delay for arrivals and a positive reduction for departures. EWR showed a substantial 

total delay benefit for arrivals over departures (i.e., 174 vs. 14 seconds or 12 times higher). TEB provided about two 

times higher benefits for arrivals over departures. 

 Mann-Whitney tests show the mTRCM provided statistically significant smaller per flight total delay than the 

baseline for N90 arrivals (p-value = 0.0002) and departures (p-value = 0.0010). For JFK, EWR, and TEB, the mTRCM 

provided statistically significant smaller per flight total delay than the baseline for arrivals and departures (p-value < 

0.05). For LGA, only for departures that the mTRCM provided statistically significant smaller per flight total delay 

than the baseline (p-value = 0.0010), while for arrivals there was no difference between the mTRCM and baseline (pvalue = 0.2352). 

Figure 6. Average (overall days) reduction in per flight total delay (baseline minus mTRCM).

 To better understand what contributed to the 12-day average total delay benefits, the total delays were further 

broken down into delay within metroplex airspace (or airborne), taxi delay, departure queue delay, and delay outside 

arrival fix. Figure 7 shows average (over 12 days) per flight delay broken down for arrivals and departures for all sites. 

 For arrivals (figure 7a), with the exception of LGA, the mTRCM provided total delay benefits over the baseline. 

For LGA, the main driver of negative total delays (penalty of using the mTRCM) was a substantial (negative) delay 

from outside arrival fix. Benefits from airborne delays were higher than those from surface (taxi) delays for all sites. 

Positive total delays were contributed by a positive benefit in airborne delay for all sites. 

 For departures (figure 7b), the mTRCM provided total delay benefits over the baseline. Benefits from surface (taxi 

plus departure queue) delays were higher than those from airborne delays for all sites. Positive total delays were largely 

the result of positive benefits in surface delay for all sites. The type of surface operations that contributed most to 

surface delays (i.e., taxi or departure queue) varied by sites. N90, JFK, and EWR showed departure queue as the 

driver, while LGA and TEB showed taxi as the driving influence. 

7% 5% 5% 6%

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6%

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4%

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0%

5%

10%

15%

20%

Arrivals Departures

12-Day Average Reduction

N90 JFK LGA EWR TEB

Site Arrivals Departures

N90 70.8 sec (7%) 54.0 sec (5%)

JFK 64.1 sec (5%) 75.6 sec (6%)

LGA -33.9 sec (-5%) 78.4 sec (6%)

EWR 174.0 sec (17%) 14.0 sec (1%)

TEB 80.3 sec (11%) 34.2 sec (4%)

13
(a) Arrivals 

(b) Departures 

Figure 7. Average (over 12 days) per flight delay breakdown (in seconds) for arrivals and departures at all 

sites. 

 

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Arrivals

Outside Fix Airborne Taxi Total

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Queue Airborne Taxi Total

14
4.2. Transit time 

 

 Figure 8 shows per flight average transit time benefits for arrivals and departures for all twelve days at the N90 

metroplex. No strong correlation was observed between demand volume and magnitude of benefit. This may be 

because the historical demand volume is not high enough to observe the strong correlation. It is anticipated that if the 

future increased demand were to be simulated, then a strong correlation would be observed. Transit time benefit varied 

by day. Generally, the mTRCM provided transit time benefit over the baseline for arrivals and departures. Nine out 

of twelve days transit time benefits for arrivals were higher than those for departures. 

Figure 8. Per flight average transit time benefit (in seconds) for arrivals and departures for all days (in Day# - 

mmddyy (day) format) at N90 metroplex.

 To understand transit time benefits at individual airports, per flight average transit time for arrivals and departures 

for all twelve days were broken down by airports (shown in figure 9). As for the overall metroplex, there was no strong 

correlation between demand volume and magnitude of benefit observed at each of the individual airports. 

 For JFK, the mTRCM provided transit time benefits over the baseline for arrivals and departures. Seven out of the 

twelve days the transit time benefits for arrivals were higher than those for departures. 

 For LGA, the mTRCM provided transit time benefits over the baseline for arrivals and departures. Nine out of the 

twelve days the transit time benefits for departures were higher than those for arrivals. 

 For EWR, all twelve days the mTRCM provided transit time benefits over the baseline for arrivals and departures. 

All twelve days the transit time benefits for arrivals were higher than those for departures. 

 For TEB, the mTRCM provided transit time benefits over the baseline for arrivals and departures. Eleven out of 

the twelve days the transit time benefits for arrivals were higher than those for departures. 

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12 - 072210 (TH) # Aircraft Benefit per Flight (seconds)

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Transit Time - Arrivals Transit Time - Departures #Arrivals #Departures

15
Figure 9. Per flight average transit time benefit (in seconds) for arrivals and departures for all days (in Day# - mmddyy (day) format) at all airports 

within N90 metroplex, all plots in a common scale.

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TEB

Transit Time - Arrivals Transit Time - Departures #Arrivals #Departures

16
 Percent reductions in transit time per flight were averaged over all twelve days and are shown in figure 10. On 

average, the mTRCM provided higher percent reductions, with respect to the baseline, for arrivals (between 5% and 

15% across all sites) than departures (between 1% and 6%). JFK showed a slightly higher percent reduction in 

departures than arrivals. In contrast, N90, LGA, EWR, and TEB showed higher percent reductions in arrivals than 

departures. EWR provided a substantial transit time benefit for arrivals over departures (i.e., 153 vs. 14 seconds or 11 

times higher), while TEB gave about more than two times higher benefit for arrivals over departures. Overall, the N90 

metroplex had 91 seconds per flight reduction (or 9%) in mean transit time for arrivals, and 54 seconds per flight 

reduction (or 5%) for departures. 

 Mann-Whitney tests show the mTRCM provided statistically significant smaller per flight transit time than the 

baseline for N90 arrivals (p-value = 0.0001) and departures (p-value = 0.0010). For JFK, LGA, EWR, and TEB, the 

mTRCM provided statistically significant smaller per flight transit time than the baseline for arrivals and departures 

(p-value < 0.05). 

Figure 10. Average (overall days) reduction in per flight transit time (baseline minus mTRCM).

4.3. Throughput efficiency (TE) 

Scatter plots of demand versus throughput efficiency (TE) for all hours and all days were generated for arrivals 

and departures. These plots were useful for checking sensitivity of the TE to traffic volume. Figure 11 shows the 

scatter plots for arrivals and departures at JFK. There was no strong trend between demand and TE. In other words, 

TE was insensitive (or robust) to traffic volume. Baseline departures had the widest range of TE (from 0.4 to 1.0). 

Historical capacity during the period of 10/2009 to 09/2010 was retrieved from the ASPM database. Depending on 

many factors such as weather and runway configuration, the Airport Acceptance Rate (AAR) and Airport Departure 

Rate (ADR) capacities can vary from hour to hour. Therefore, they are considered as random variables with underlying 

distributions. The 95% coverage interval9

 of the historical capacity distribution was used to determine how close the 

traffic volume used in this study was to the capacity during the same time period. This interval is shown on the figure 

(green vertical lines). For arrivals, 95% of AARs were between 28 and 52 aircraft per hour. For departures, 95% of 

ADRs were between 22 and 52 aircraft per hour. It is observed that both arrival and departure processes had data 

points scattered within and beyond these ranges; therefore, JFK operated at its historical capacity. This implies that if 

increased traffic volume is expected in the future, an improvement in capacity (or equivalent to a TE improvement) at 

JFK is needed. 

 

9

 95% Coverage interval indicates that 95% of hourly throughputs fall within this interval. 

9%

5% 5% 6% 7% 6%

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Average Reduction

N90 JFK LGA EWR TEB

Site Arrivals Departures

N90 90.8 sec (9%) 54.0 sec (5%)

JFK 69.5 sec (5%) 75.6 sec (6%)

LGA 47.4 sec (7%) 78.4 sec (6%)

EWR 153.0 sec (15%) 14.0 sec (1%)

TEB 80.0 sec (11%) 34.2 sec (4%)

17
Figure 11. Scatter plots for all tested days at JFK with the 95% coverage intervals of historical Airport Acceptance Rate and Airport Departure Rate 

(green vertical lines).

Figure 12. Scatter plots for all tested days at LGA with the 95% coverage intervals of historical Airport Acceptance Rate and Airport Departure 

Rate (green vertical lines).

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18
Figure 13. Scatter plots for all tested days at EWR with the 95% coverage intervals of historical Airport Acceptance Rate and Airport Departure 

Rate (green vertical lines). 

Figure 14. Scatter plots for all tested days at TEB with the 95% coverage intervals of historical Airport Acceptance Rate and Airport Departure Rate 

(green vertical lines). 

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19
Figures 12 to 14 show similar plots for LGA, EWR, and TEB. There was no strong trend between demand and TE 

at these three airports. For LGA arrivals (figure 12), 95% of AARs were between 26 and 44 aircraft per hour. For 

departures, 95% of ADRs were between 26 and 40 aircraft per hour. Both arrival and departure processes were also 

at their historical capacities. For EWR arrivals (figure 13), 95% of AARs were between 32 and 52 aircraft per hour. 

For departures, 95% of ADRs were between 32 and 48 aircraft per hour. Both arrival and departure processes were 

also at their historical capacities. For TEB arrivals and departures (figure 14), 95% of AARs and ADRs were between 

20 and 32 aircraft per hour. Both arrival and departure processes were far less than their historical capacities. This 

implies that TEB can generally absorb higher traffic volume without any capacity improvement to its current 

operation. In contrast, LGA and EWR were at their historical capacities, and were in need of any capacity improvement 

in order to absorb higher traffic volumes. 

 Figure 15 shows mean (over all hours) TEs for arrivals and departures for all twelve days at the N90 metroplex. 

TE varied by days, type of operation (arrival vs. departure), and runway configuration schedules (baseline vs. 

mTRCM). Upon inspection of the figure, TEs for arrivals were generally smaller than those for departures. Using the 

mTRCM schedule usually provided better TEs over the baseline schedule for arrivals and departures. In the baseline 

case, the TEs for arrivals varied in the range of 0.70 and 0.92; for the mTRCM case, TE for arrivals were in the range 

of 0.81 and 0.94. For departures, the baseline case resulted in TEs in the range of 0.85 and 0.93, while for the mTRCM 

case, they were in the range of 0.88 and 0.93. The mTRCM provided smaller TE variations (narrower ranges) than the 

baseline for both arrivals and departures. Comparing variations between arrivals and departures, departures showed 

smaller variations in TE than arrivals for both runway configuration schedules. This implies a more robust operation 

efficiency for departures than arrivals regardless of runway schedules. 

Figure 15. Mean (over all hours) throughput efficiencies (TE) at N90 metroplex for arrivals and departures 

for all days (in Day# - mmddyy (day) format).

 To understand TEs at individual airports, mean (over all hours) TEs for arrivals and departures for all twelve days 

were next broken down by airports (shown in figure 16). TE varied by days, type of operation (arrivals vs. departures), 

runway schedules (baseline vs. mTRCM), and airports. Similar to N90 observations, TEs for arrivals were smaller 

than those for departures at all airports. The mTRCM schedule provided better TEs over the baseline for arrivals and 

departures. Table 5 summarizes the TE ranges, standard deviation, and mean (over all days) by type of operation, 

runway configuration schedules, and airports. The mTRCM schedule produced smaller TE variations (smaller 

0.60

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Throughput Efficiency

N90

TE - Baseline - Arrivals TE - Baseline - Departures

TE - mTRCM - Arrivals TE - mTRCM - Departures

20
standard deviation) than the baseline for arrivals and departures at all airports. Similar to N90 observations, departures 

showed smaller TE variations than arrivals for both runway schedules. 

Table 5. Throughput efficiency ranges (min, max), standard deviation (SD), and mean (over all hours and 

days) by type of operation (arrivals vs. departures), runway schedules (baseline vs. mTRCM), and airports. 

 

Airport Type of 

Operation

Baseline Schedule mTRCM Schedule

Range SD Mean Range SD Mean

N90 Arrivals 0.70-0.92 0.06 0.75 0.81-0.94 0.04 0.84

Departures 0.85-0.93 0.02 0.88 0.88-0.93 0.01 0.91

JFK Arrivals 0.68-0.92 0.08 0.76 0.79-0.97 0.05 0.84

Departures 0.84-0.93 0.03 0.88 0.88-0.92 0.02 0.91

LGA Arrivals 0.66-0.90 0.07 0.72 0.73-0.89 0.04 0.78

Departures 0.80-0.92 0.04 0.84 0.86-0.95 0.03 0.89

EWR Arrivals 0.64-0.88 0.06 0.73 0.79-0.90 0.04 0.84

Departures 0.87-0.96 0.03 0.89 0.86-0.96 0.02 0.90

TEB Arrivals 0.79-0.99 0.07 0.84 0.87-0.98 0.04 0.93

Departures 0.87-0.97 0.03 0.92 0.94-0.97 0.01 0.95

 Improvements in the mean TEs are shown in figure 17 as percent improvement with respect to the baseline. On 

average, the mTRCM provided higher percent improvements for arrivals (between 9 and 16 percent across all sites) 

than departures (between 1 and 6 percent). Especially, EWR showed a substantial TE improvement for arrivals (16%). 

Overall, the N90 metroplex provided 12% TE improvement for arrivals, and 3% improvement for departures. 

 Mann-Whitney tests show that the mTRCM provided statistically significant higher TEs than the baseline for N90 

arrivals (p-value = 0.0004) and departures (p-value = 0.0015). For JFK, LGA, and TEB, the mTRCM schedule 

provided statistically significant higher TEs than the baseline for arrivals and departures (p-value < 0.05). For EWR 

arrivals the mTRCM provided statistically significant higher TEs than the baseline (p-value = 0.0002), while for 

departures there was no TE difference between the mTRCM and baseline (p-value = 0.097). 

21
Figure 16. Mean (over all hours) throughput efficiencies (TE) for arrivals and departures for all days (in Day# - mmddyy (day) format) at JFK, LGA, 

EWR, and TEB.

0.60

0.70

0.80

0.90

1.00

1 - 100609 (T)

2 - 101709 (SA)

3 - 112009 (F)4 - 011010 (S)

5 - 030910 (T)

6 - 032510 (TH)7 - 050610 (TH)

8 - 051810 (T)

9 - 060510 (SA)

10 - 070310 (SA)

11 - 071310 (T)

12 - 072210 (TH)

Throughput Efficiency

JFK

TE - Baseline - Arrivals TE - Baseline - Departures

TE - mTRCM - Arrivals TE - mTRCM - Departures

0.60

0.70

0.80

0.90

1.00

1 - 100609 (T)

2 - 101709 (SA)

3 - 112009 (F)4 - 011010 (S)

5 - 030910 (T)

6 - 032510 (TH)7 - 050610 (TH)

8 - 051810 (T)

9 - 060510 (SA)

10 - 070310 (SA)

11 - 071310 (T)

12 - 072210 (TH)

Throughput Efficiency

LGA

TE - Baseline - Arrivals TE - Baseline - Departures

TE - mTRCM - Arrivals TE - mTRCM - Departures

0.60

0.70

0.80

0.90

1.00

1 - 100609 (T)

2 - 101709 (SA)

3 - 112009 (F)4 - 011010 (S)

5 - 030910 (T)

6 - 032510 (TH)7 - 050610 (TH)

8 - 051810 (T)

9 - 060510 (SA)

10 - 070310 (SA)

11 - 071310 (T)

12 - 072210 (TH)

Throughput Efficiency

EWR

TE - Baseline - Arrivals TE - Baseline - Departures

TE - mTRCM - Arrivals TE - mTRCM - Departures

0.60

0.70

0.80

0.90

1.00

1.10

1 - 100609 (T)

2 - 101709 (SA)

3 - 112009 (F)4 - 011010 (S)

5 - 030910 (T)

6 - 032510 (TH)7 - 050610 (TH)

8 - 051810 (T)

9 - 060510 (SA)

10 - 070310 (SA)

11 - 071310 (T)

12 - 072210 (TH)

Throughput Efficiency

TEB

TE - Baseline - Arrivals TE - Baseline - Departures

TE - mTRCM - Arrivals TE - mTRCM - Departures

22
Figure 17. Average (over all days) improvement in throughput efficiency (ratio of mTRCM and baseline).

4.4. Runway configuration changes 

 Although runway configuration changes were not a designated metric for this study, the frequency of changes 

recommended by the mTRCM was evaluated for its operational feasibility. Typically, configuration changes can be 

categorized as: (1) a change in the primary direction of arrival and departure flows; and (2) a change in how runways 

are used within the same primary flow direction. Examples of the former category are a change in the southeast

(13R/13L) to northwest (31L/31R) direction, or a change in the northeast (4L/4R) to southeast (13R/13L) direction. 

Examples of the latter category are a change in runway usage from an arrival (or departure) runway to a mixed-use 

runway for both arrivals and departures (i.e., from A_31R|D_31L to 31R, 31L|31L), or an additional runway to the 

existing primary flow direction (i.e., from 13L|13R to 13L, 22L|13R). 

 The first category, a change in the primary flow direction, affects arrivals more than departures. Because of the 

way arrivals are routed into terminal areas, there must be enough airspace to allow published routes to be flown 

through the TRACON airspace and proper airport traffic patterns to be flown, including any additional vectoring or 

path-stretching that might be needed for spacing considerations. A change in the primary flow direction must account 

for all of these, especially for any flights that would have to be re-routed in order to accommodate the new runway 

configuration. Because arrivals are affected more by changes in flow direction, the changes to flow direction that were 

counted for this study were those associated with a change in the primary arrival runways used. In contrast, the second 

category, a change in active runways within the same flow direction, requires less coordination among flights 

(especially for arrivals), airspace, and airport traffic pattern than the change in the flow direction. 

12%

3%

10%

2%

9%

6%

16%

1%

11%

4%

0%

5%

10%

15%

20%

Arrivals Departures

12-Day Avg. TE Improvement

N90 JFK LGA EWR TEB

23
Figure 18. Number of configuration changes at JFK, LGA, EWR, and TEB for all tested days.

0

2

4

6

8

10

12

14

16

18

20 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 Day 11 Day 12 Number of changes

JFK

Baseline - Flow Baseline - Usage mTRCM - Flow mTRCM - Usage

0

2

4

6

8

10

12

14

16 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 Day 11 Day 12 Number of changes

LGA

Baseline - Flow Baseline - Usage mTRCM - Flow mTRCM - Usage

0

2

4

6

8

10

12

14 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 Day 11 Day 12 Number of changes

EWR

Baseline - Flow Baseline - Usage mTRCM - Flow mTRCM - Usage

0

2

4

6

8

10

12

14

16 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 Day 11 Day 12 Number of changes

TEB

Baseline - Flow Baseline - Usage mTRCM - Flow mTRCM - Usage

24
 Figure 18 shows the number of configuration changes resulting from using the mTRCM schedule; changes in flow 

direction (the first category) are labelled as “Flow” and changes in runway usage within the same flow direction (the 

second category) are labelled as “Usage”. The corresponding historical number of changes (i.e., the baseline’s 

changes) that occurred on those days are also shown. The number of configuration changes varied by days, change 

categories (“Flow” vs. “Usage”), runway schedules (baseline vs. mTRCM), and airports. Table 6 summarizes the 

ranges, standard deviation, and mean (over all days) of the number of configuration changes by change categories, 

runway schedules, and airports. The mTRCM resulted in higher day-to-day variations (larger ranges and standard 

deviations) than the baseline case at all airports for both change categories. Additionally, the mTRCM had higher 

average number of changes per day than the baseline for both change categories. The higher average number per day 

recommended by the mTRCM is theoretically anticipated because the algorithm is able to take all flight, weather, and 

airport information into account while trading off the airborne, taxi, and queue (departing aircraft only) time durations 

for the minimum total time. This trade off calculation is usually complex and may be beyond operator’s capability 

without significantly increasing the workload. The right balance between the operator’s acceptable changes per day 

and the expected benefits needs to be further investigated so that the number of changes are not excessive. 

Table 6. Ranges (min, max), standard deviation (SD), and mean (over all days) for number of configuration 

changes by change categories (flow vs. usage change), runway schedules (baseline vs. mTRCM), and airports. 

Change Category Airport Baseline Schedule mTRCM Schedule

Range SD Mean Range SD Mean

Flow Change JFK 0 - 5 1.60 1.75 6 - 18 4.27 12.92

LGA 0 - 6 1.80 2.17 4 - 14 3.48 8.58

EWR 0 - 3 0.97 0.75 0 - 12 3.27 8.17

TEB 0 - 6 1.85 2.17 2 - 15 3.81 8.17

Usage Change JFK 1 - 5 1.31 2.58 2 - 15 3.41 6.25

LGA 0 - 3 1.04 1.00 0 - 5 1.45 1.50

EWR 0 - 4 1.68 1.92 2 - 9 2.39 5.92

TEB 0 - 2 0.87 0.75 0 - 13 4.78 6.92

 To better understand the airport-specific configuration changes, the frequency of changes by category across all 

days was plotted for historical (baseline) and the mTRCM recommended runway schedules (shown in figure 19). For 

JFK, the mTRCM generated schedules suggested 67% of total changes as changes in the flow direction and the other 

33% as changes in runway usage. In contrast, JFK historically (i.e., the baseline schedules) executed more changes 

in runway usage (60% of total) than flow direction changes. At LGA, the mTRCM recommended more flow direction 

changes (85%) than runway usage changes. This was also true historically. That is, 68% of total historical (baseline) 

changes were changes in flow direction. In practice, JFK and LGA usually operate in the same flow direction. In 

addition, historically JFK had higher average traffic volume per day than LGA (from table 4). It is speculated that 

approach controllers at the N90 chose to change the flow direction at LGA to follow the flow direction at JFK, as 

observed by this statistics. This speculation will need further investigation. 

 EWR had more flow direction changes than runway usage changes recommended by the mTRCM (58%). In 

contrast, 72% of total historical changes at EWR were runway usage changes. TEB had more flow direction changes 

for both the historical schedule (74%) and the mTRCM schedule (54%) than changes in runway usage. In practice, 

TEB operations have a strong interaction with EWR operations. Given the traffic volume at TEB was much lower 

than that at EWR historically (table 4), it is suspected that traffic flow managers chose to change the flow direction at 

TEB to accommodate the operations at EWR. This speculation will,again, need to be investigated further. 

Overall, the mTRCM preferred a change in “Flow” over a change in “Usage” categories, while historically the 

preference varied by airports. Ratios of the mean change frequencies per day between the mTRCM and the baseline 

at all airports are given in figure 20. The mTRCM recommended 7 times the historical flow direction changes at JFK; 

4 times at LGA; 11 times at EWR; and 4 times at TEB. In comparison, the mTRCM recommended twice the historical 

changes in runway usage at JFK; twice at LGA; 3 times at EWR; and 9 times at TEB. These values were clearly 

excessive compared to what is normally found at these airports, especially for the flow direction change. The mTRCM 

currently does not take into account the costs associated with a flow direction change whereas air traffic personnel are 

keenly well aware of the costs. Further refinement in the algorithm will need to incorporate these costs. 

25
 

 

Figure 19. Breakdown of number of configuration changes by categories (flow vs. usage changes) at JFK, 

LGA, EWR, and TEB.

 

Figure 20. Ratio of the mean (per day) number of configuration changes between mTRCM and baseline.

 

7

2

4

2

11

3

4

9

0

2

4

6

8

10

12

Flow Change Usage Change

Ratio of mean # Changes 

JFK LGA EWR TEB

26
5. Conclusions and future research 

The System-Oriented Runway Management (SORM) concept is a collection of capabilities focused on a more 

efficient use of runways while considering all of the factors that affect runway use. Examples of these factors are: 

surface wind velocity, meteorological conditions, traffic demand, adjacent airport traffic flows, severe weather 

activity, environmental factors, intersecting arrival/departure runways, distance between arrival runways, etc. Tactical 

Runway Configuration Management (TRCM), one of the SORM capabilities, provides runway configuration and 

runway usage recommendations, and monitoring the active runway configuration for suitability given existing factors. 

This report focuses on the metroplex environment, with two or more proximate airports having arrival and departure 

operations that are highly interdependent. The myriad of factors that affect metroplex opeations require consideration 

in arriving at runway configurations that collectively best serve the system as a whole. There are two metroplex TRCM 

(mTRCM) algorithms currently available; one was provided by Mosaic ATM under a contract to NASA (referred to 

as “mTRCM-A”), and the other has been developed by LaRC researchers (referred to as “mTRCM-B”). The mTRCMA algorithm uses Mixed Integer Linear Programming (MILP) to solve each airport independently for the airportspecific optimal runway configurations. Because each airport is solved independently, this algorithm does not 

adequately capture interdependent metroplex conflicts. In comparison, the mTRCM-B captures these conflicts by 

referencing a list of excluded metroplex configuration combinations. The mTRCM-B uses this list during the second 

of two sequential enumerative searches. The first search is performed for all airports within the metroplex 

independently, resulting in airport-specific lists of all configurations with their associated best change times. These 

lists along with the excluded metroplex configuration list are then given as inputs to the second enumerative search, 

which generates the optimum metroplex configuration that is free of interdependent metroplex conflicts. 

This study first compared the mTRCM-A and mTRCM-B algorithms using the same traffic datasets provided by 

Mosaic ATM for the New York metroplex (or N90). For this study, there are four airports considered as part of the 

N90 (New York Terminal Radar Approach Control, or TRACON) metroplex: John F. Kennedy International Airport 

(JFK), LaGuardia Airport (LGA), Newark Liberty International Airport (EWR), and Teterboro Airport (TEB). Based 

on this comparison, the mTRCM-B provided configuration change schedules generating consistently higher per flight 

transit time savings than the savings from the mTRCM-A. A benefit assessment of total delay, transit time, and 

throughput efficiency (TE) was performed using the mTRCM-B algorithm on twelve representative days during 2009-

2010, using traffic volumes from N90. N90 was selected for its complexity in both airport configuration decisions and 

runway assignment and sequencing decisions. To assess the mTRCM-B benefit, the performance metrics must be 

compared with the actual historical operations. 

To assess the mTRCM-B benefit, the performance metrics must be compared with the actual historical operations. 

The historical configuration schedules can be viewed as the schedules produced by subject matter experts (SMEs), 

and therefore are referred to as the SMEs’ schedules. These schedules were obtained from the FAA’s Aviation System 

Performance Metrics (ASPM) database; this is the most represenative information regarding runway configuration 

selection by SMEs. All twelve traffic days were run once in the SORM software using the historical runway 

configurations, and once with the mTRCM-B-generated runway configuration schedule.

 Total delay, transit time, and throughput efficiency (TE) benefits varied by days, by airports, and by type of 

operation (arrivals vs. departures). For total delay, the mTRCM-B’s runway configuration schedules statistically 

showed better results than those reflected in the SMEs’ schedules for departures at all airports. For arrivals, the 

mTRCM-B’s statistically showed better results than those reflected in the SMEs’ schedules at the New York metroplex 

(N90), JFK, EWR, and TEB. There was no statistical difference in total delay between both schedules for LGA 

arrivals. N90 observed an average (over all days) total delay benefit of 70.8 seconds (or 7% benefit) per arrival and of 

54 seconds (or 5% benefit) per departure. For transit time, the mTRCM-B’s schedules statistically outperformed the 

SMEs’ schedules for all airports and all service types. N90 observed an average (over all days) transit time benefit of 

90.8 seconds (or 9% benefit) per arrival and of 54 seconds (or 5% benefit) per departure. For TE improvement, the 

mTRCM-B’s schedules statistically outperformed the SMEs’ schedules for arrivals and departures at N90, JFK, LGA, 

and TEB. For EWR, the mTRCM-B’s schedule provided a statistically better TE than the SMEs’ only for arrivals; for 

departures there was no statistical difference in TE between both schedules. Over all days, the average improvement 

for N90 was 12% for arrivals and 3% for departures. 

 Although the number of runway configuration changes was not initially a metric for this assessment, it was 

evaluated to determine whether or not the number of changes recommended by the mTRCM-B was operationally 

feasible. Using the same scheme as in a previous study [Ref. 1], configuration changes were categorized as: (1) a 

change in the primary direction of arrival flows (referred to as “Flow”); and (2) a change in how runways are used 

27
within the same primary flow direction (“Usage”). The frequency of configuration changes varied by days, change 

categories (“Flow” vs. “Usage”), runway schedules (historical SMEs’ vs. mTRCM-B’s), and airports. Overall, the 

mTRCM-B preferred a change in “Flow” over a change in “Usage” categories, while historically the preference varied 

by airports. In terms of change frequencies, the mTRCM-B recommended higher averages per day (as high as 11 

times) than the SMEs. These change frequencies were clearly excessive compared to what is normally found at 

airports throughout the National Airspace System (NAS), especially for the flow direction change. It is recommended 

that, as part of future algorithm improvements, the mTRCM-B should be adjusted to limit the number of changes per 

day to what would be considered a more acceptable number. Additionally, because a list of interrelated metroplex 

conflicts has been captured in the current mTRCM-B algorithm, future improvement should also include airspace 

coordinations replicating more realistic metroplex operations. 

Reference 

[1] Oseguera-Lohr, Rosa, Phojanamongkolkij. Nipa, Lohr, Gary, “Benefits Assessment for Single-Airport Tactical Runway 

Configuration Management Tool (TRCM),” NASA/TM–2015-218700, March 2015. 

[2] Oseguera-Lohr, Rosa M., Phojanamongkolkij, Nipa, Lohr, Gary W., and Fenbert, James W. , “Benefits Assessment for Tactical 

Runway Configuration Management Tool,” AIAA 2013-4395, presented at the 2013 Aviation Technology, Integration, and 

Operations Conference, August 12-14, 2013, Los Angeles, CA. 

[3] Mosaic ATM, Inc., “SORM System Test Document” (internal deliverable document), contract number 

NNL09AA02B/ NNL09AB21T, April 30, 2012.

[4] Mosaic ATM, Inc., “SORM Algorithm Description Document” (internal deliverable document), contract number 

NNL09AA02B/ NNL09AB21T, April 30, 2012. 

[5] Phojanamongkolkij, N., Robbins, S. W., Fenbert, J.W., and Hartman, C.L., “Algorithm Assessment for Metroplex Tactical 

Runway Configuration Management (mTRCM),” internal paper, October 2013. 

[6] Cheng, F., Gulding, J., Baszczewski, B., and Galaviz, R., “An Optimization-Based Sample Day Selection Algorithm for Future 

Schedule Generation,” Eleventh AIAA Aviation, Technology, Integration, and Operations (ATIO) Conference, AIAA 2011-7054, 

Virginia Beach, VA, September 2011. 

[7] United States Government Accountability Office, April 2013, “NextGen Air Transportation System, Report to Congressional 

Requesters” 71 pp., GAO-13-264.

[8] Hopp, W. and Spearman, M., Factory Physics: Foundations of Manufacturing Management, Chapter 9, Irwin, 1996, pp. 282-

315. 

[9] Gawdiak, Y., Carr, G., and Hasan, S., “JPDO Case Study of NextGen High Density Operations,” 9th AIAA Aviation 

Technology, Integration, and Operations (ATIO) Conference, 2009. 

[10] Chandrasekar, S., Shin, S., and Hwang, I., “Throughput Metric Development and Analysis for the National Airspace System,” 

12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 2012. 

[11] Federal Aviation Administration: The Traffic Flow Management Learning Center. 

http://tfmlearning.fly.faa.gov/NY_Airspace/NY_Airspace_Pkg/NY_Airspace.swf. Access January 9, 2014. 

[12] Joint Project Development Office, “Concept of Operations for the Next Generation Air Transportation System,” Version 2.0, 

June 13, 2007. 

[13] Montgomery, D. and Runger, G., Applied Statistics and Probability for Engineers, John Wiley & Sons, Inc., 1994. 

28
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Benefit Assessment for Metroplex Tactical Runway Configuration 

Management (mTRCM) in a Simulated Environment 

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 6. AUTHOR(S)

Phojanamongkolkij, Nipa; Osequera-Lohr, Rosa M.; Lohr, Gary W.; 

Robbins, Steven W.; Fenbert, James W.; Hartman, Christopher L.

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NASA Langley Research Center

Hampton, VA 23681-2199

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National Aeronautics and Space Administration

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14. ABSTRACT

The System-Oriented Runway Management (SORM) concept is a collection of capabilities focused on a more efficient use of runways while considering all of 

the factors that affect runway use. Tactical Runway Configuration Management (TRCM), one of the SORM capabilities, provides runway configuration and 

runway usage recommendations, and monitoring the active runway configuration for suitability given existing factors. This report focuses on the metroplex 

environment, with two or more proximate airports having arrival and departure operations that are highly interdependent. The myriad of factors that affect 

metroplex opeations require consideration in arriving at runway configurations that collectively best serve the system as a whole. To assess the metroplex 

TRCM (mTRCM) benefit, the performance metrics must be compared with the actual historical operations. The historical configuration schedules can be 

viewed as the schedules produced by subject matter experts (SMEs), and therefore are referred to as the SMEs’ schedules. These schedules were obtained from 

the FAA’s Aviation System Performance Metrics (ASPM) database; this is the most representative information regarding runway configuration selection by 

SMEs. This report focused on a benefit assessment of total delay, transit time, and throughput efficiency (TE) benefits using the mTRCM algorithm at 

representative volumes for today’s traffic at the New York metroplex (N90). 

15. SUBJECT TERMS

Benefit; Metroplex; Runway Managment

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