March 2011 

NASA/CR–2011-217066 

Investigation of the Impact of User Gaming in 

the Next Generation National Airspace System 

George C. Hunter and Huina Gao 

Sensis Corporation, Seagull Technology Center, Campbell, California 
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March 2011 

NASA/CR–2011-217066 

Investigation of the Impact of User Gaming in 

the Next Generation National Airspace System 

George C. Hunter and Huina Gao 

Sensis Corporation, Seagull Technology Center, Campbell, California 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

Acknowledgments 

The authors would like to thank Dr. Lance Sherry of George Mason University for his 

assistance and management of this project, Mr. Diego Escala for the software design and 

development of the gaming strategies, Dr. Natalia Alexandrov of NASA for her oversight 

and technical guidance, and WSI for their aviation weather and traffic data used in this 

study.
iii 

Table of Contents 

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

1.1 Executive summary findings ....................................................................................2 

1.2 Executive summary recommendations ....................................................................4 

2 Design of Hypothetical NextGen Gaming Strategies ..............................................4 

2.1 Simulation platform .................................................................................................5 

2.2 Model functional requirements ................................................................................6 

2.3 Model design ............................................................................................................7 

3 Investigation of Impact of Gaming Strategies .......................................................21 

3.1 Experiment outline .................................................................................................21 

3.2 Gaming strategies ...................................................................................................24 

3.3 Results ....................................................................................................................26 

4 Conclusions and Recommendations ......................................................................40 

4.1 Conclusions ............................................................................................................40 

4.2 Research recommendations ...................................................................................42 

A Appendix ................................................................................................................43 

References ..........................................................................................................................44 
1 

1 Introduction 

Over the past three decades, growth in the demand for air transportation has exceeded the growth 

in the national airspace system (NAS) capacity. In recent years the NAS traffic volume has been 

near capacity. Systems operating near capacity inevitably have delays and it is no surprise that 

NAS delays have increased in recent years. The desire to minimize delay costs has placed focus 

and attention on the NAS air traffic management (ATM) system. Substantial research and 

development activities have been underway to make the ATM system as efficient as possible. 

In dealing with the increased demand, the ATM system has had to delay or reroute larger 

numbers of flights. The ATM problem, however, has a large number of different solutions that 

may be used to achieve similar levels of system throughput and delay. But these solutions are not 

all equal as judged by NAS operators. User business models are not easily accounted for in the 

ATM solution, so an ATM solution that produces good overall throughput and delay may be 

undesirable according to users if, for instance, high-value flights are delayed where lower valued 

flights could have been delayed. Or again, users may have preferred routings which the ATM 

system is not aware of. 

One initiative that has helped to provide user representation in the ATM solution is the 

collaborative decision making (CDM) process [1]. The CDM process addresses this issue by 

bringing users (referred to here as airline operation centers [AOCs]) and ATM providers together 

for information exchange and cooperative planning. Such cooperative planning has been 

instituted, for instance, for the purpose of planning airport slot control strategies and rerouting 

strategies. While the CDM initiatives have met with much success, they have also introduced the 

potential for AOCs to manipulate the system in unforeseen, unintended, and perhaps undesirable 

ways, from a system-wide, synoptic perspective. 

This type of manipulation is sometimes referred to as “gaming” the system. For instance, under 

this definition one gaming example is an operator requesting to use a particular capacity element 

(gate, runway, airspace sector, etc.) of the system with the hidden intention of not using that 

particular element. The reason for this action could be that the AOC knows that the element has 

high value to a competitor. 

On the other hand, an AOC merely deciding on whether or not to use a system capacity element, 

and factoring in the likely actions of other operators in the system, would not necessarily be 

referred to as gaming. This study is not concerned with determining an appropriate definition for 

gaming. Instead, we investigate several models of user decision making behavior which could be 

considered to be gaming behavior. 

The potential for gaming behavior in the future NAS will likely grow as decision making is 

increasingly distributed to users. For instance, increased levels of user involvement in the traffic 

flow management process may evolve via a number of mechanisms, including increased 

collaboration and information sharing, improved mechanisms for indicating preferences, and 

market-based traffic flow management. 
2 

The goal of this study is first to develop a high-fidelity simulation tool to allow researchers to 

investigate various aspects of traffic flow management (TFM) user decision making, and second 

to use the tool to perform a study of NAS gaming. Key questions are: To what extent can user 

decision-making degrade system performance? Can the system be configured to dis-incentivize, 

limit, or avoid undesirable user decision making that degrades system performance or leads to 

system instabilities such as a “race-to-the-bottom” scenario? 

This study partially addresses these questions by designing, programming and testing different 

decision making strategies into the AOCs. These schemes are not tailored for specific scenarios, 

but rather are designed to represent different algorithmic approaches, in general, that AOCs 

apply throughout their NAS-wide operations. We then investigate emergent system dynamics 

and interactions between AOCs and TFM. 

1.1 Executive summary findings 

This section summarizes the findings of this study. The findings are as follows. 

Significant performance variations in gaming scenarios. Our results suggest that NextGen 

gaming does not reduce to a few principles. Several different gaming strategies can be 

envisioned, and each strategy has its own variants. For instance, the competitive-15 versus 

competitive-30 strategies shows significant differences, with the 15 minute time horizon showing 

much better performance. Furthermore, different users can select from different strategies, and 

very likely will use multiple strategies simultaneously in different scenarios. These many 

possible combinations make for an enormous problem space. In our investigation so far of this 

problem, we find that the performance of the different user groups can vary dramatically along 

these dimensions. Slight modifications in a strategy (such as the competitive strategy look ahead 

time, see below), or a relatively minor increase in the number of users implementing a strategy 

can significantly alter results. Also, when different users implement the same strategy, one user 

may experience delay reduction while the other experiences a delay increase. Their different 

flight schedules, for instance, can cause such different results. We experimented with limited 

numbers of gaming users and strategies, but even in that limited problem space found significant 

performance variations. While some trends are evident, our results suggest the NextGen gaming 

problem is not easily generalized into a relatively few, simple principles. 

NAS performance degradation. Gaming strategies often degrade the performance of other AOCs 

and non AOC flights, and most gaming combinations of the two AOCs degraded the overall 

NAS performance. Not surprisingly, one AOC performance can change radically depending on 

the strategy of the other AOC. 

Increase in system congestion. Gaming strategies are not concerned with managing demand 

within capacity constraints, and in general cause increases in airspace or airport congestion. In 

our experiments, all but the cooperative behavior aggravate system congestion. The observed 

congestion increases are not a major operational impact. Nonetheless, it is an impact of the 

gaming strategies and reveals that such gaming scenarios involve a third “player.” In addition to 

the two AOCs (or more generally N AOCs), there is also the service provider that may be 

impacted by the shifting of the demand profile and thus increase in congestion. That is, NASwide user gaming can be viewed as a third-party game: the participating airlines, non-
3 

participating airlines, and the service provider. Of course such user-induced congestion is 

possible only to the extent that users can influence the strategic TFM initiatives. 

Unintended consequences. In addition to increasing delay of other AOC flights and non AOC 

flights, and aggravating system congestion, gaming strategies can ultimately have the unintended 

consequence of aggravating own fleet total delay. This consequence could serve as a gaming 

disincentive if TFM service provider strategies could be designed to ensure that such unintended 

delay occurs. 

Categories of gaming effects. We began this study with three categories that describe the user 

intent: cooperative, competitive and strategic. Our results suggest different types of outcomes 

that do not map to these intent categories. The effects are more complicated given that there are 

various combinations of strategies implemented by the users. We found that the results can be, at 

least in principle, categorized into four qualitative categories which are helpful in describing and 

grouping the impact of the gaming actions. First, a strategy (operating within a given 

environment) may improve its own performance and also improve the overall NAS. We refer to 

such a strategy as egalitarian. Second, a strategy may improve its own performance, while 

degradation to other flights, if any, is less significant. Therefore the overall NAS performance 

improves. We refer to such a strategy as utilitarian. Third, a strategy may improve its own 

performance, while degradation to other flights is more significant. Therefore, the overall NAS 

performance degrades. We refer to such a strategy as aggressor. Finally, a strategy may degrade 

its own performance, while degradation to other flights is more significant. Obviously the overall 

NAS performance degrades. We refer to such a strategy as spoiler. 

Best strategies. The best gaming performance, for an AOC’s own flights and for the overall NAS 

delay, we found, was produced by the competitive-15 strategy. The strategic-surplus and 

cooperative were the next best, providing slight aggregate improvement. The strategic-WCRD 

provided enhanced or degraded results, depending on the other AOC-specific operating 

characteristics. 

Dynamic strategies. The effectiveness of AOC strategies depends on the strategies in use by the 

other users. This suggests that dynamic strategies could be designed to react to the strategies 

currently in use by the other AOCs. This could induce dynamic strategies to be implemented by 

other AOCs. 

Race the bottom. The possibility of gaming strategies reacting to other gaming strategies brings 

with it a range of possible system evolutionary pathways, including both equilibrium and 

divergent outcomes. For instance, one possibility is the race-to-the-bottom scenario, where 

gamers counter each other with spoiler strategies. The NAS devolves to further levels of 

decreasing performance as users successively activate such gaming strategies. 

Hybrid strategies. Sophisticated users would likely implement more complex hybrid strategies 

which simultaneously pursuing different strategies for different markets, or even different flights, 

depending on the tactical scenario. 
4 

1.2 Executive summary research recommendations 

This section summarizes questions raised by the findings outlined in the previous section, as well 

as the recommendations for further research. The recommendations are as follows. 

Investigate causality. More research is required to determine the underlying causality of how 

gaming actions affect the performance of other AOC flights and overall NAS performance. How 

exactly is performance degraded or improved, and how can these causes be dis-incentivized and 

incentivized, respectively? 

Evaluate flight delay cost outcomes. In addition to the delay metric, future NextGen gaming 

research should evaluate the effect of gaming strategies on the flight delay cost results of the 

AOCs and non AOC flights. Is it possible, for instance, to degrade delay performance but 

improve (reduce) the overall flight delay cost? 

Refined investigations. We have developed various gaming strategies, but further levels of detail 

are needed to ensure these strategies are realistic. Also, additional strategies should be 

considered. 

Investigate the problem space. As we concluded above, the problem space is complex and large. 

Further investigation is required to explore the space and learn more about the impact of 

NextGen gaming strategies. 

Investigate incentivizing users. Further research is required to investigate service provider 

strategies and rules of the road to (i) create a gaming equilibrium so as to avoid undesirable 

dynamics, chaos, and race-to-the-bottom scenarios, and (ii) force users away from undesirable 

gaming strategies such as spoiler and aggressor strategies. 

Model dynamic strategies. Research is required to model and evaluate dynamic users who do not 

adhere to a static strategy, but use a time-varying strategy which switches between strategies 

depending on the actions of the other AOCs. 

Model hybrid strategies. Research is required to model and evaluate hybrid users who do not 

adhere to a global strategy, but use a dynamic hybrid strategy which simultaneously implements 

different strategies in different markets or flights, depending on which is most profitable for each 

given scenario. 

2 Design of Hypothetical NextGen Gaming Strategies 

This section describes our design of three different categories of gaming strategies for 

experimentation: cooperative, competitive and strategic. Cooperative strategies consider 

proposed delays or reroutes from the TFM service provider. Competitive strategies derive 

alternative delays or reroutes and strategic strategies consider competing traffic. 

As this section shows, our strategies are based on high-fidelity models. The flight delay cost 

includes several detailed components and they are applied to each flight individually. 
5 

Furthermore, the decision making strategies are based on detailed flight information both for 

own flights and for the flights of other AOCs. This flight information includes the flight 

congestion cost, broken down by airspace and airport components. And the decision-making 

strategies derive actions at the flight level. 

This high level of fidelity supports accurate gaming models. Individual AOCs, however, are 

concerned with a wide range of detailed operational, cost and marketing factors that are beyond 

the scope of this study. For instance, their operational decisions can be influenced by the 

presence of very important person (VIP) passengers, which we do not model in this study. 

2.1 Simulation platform 

We use the Probabilistic NAS Platform (PNP) simulation tool [2-3] to investigate the gaming 

problem in this effort. PNP supports the modeling and simulation of AOC decision making and 

gaming in a meta-simulation environment. The architecture and design of the future NAS is not 

yet precisely defined, but increasing levels of CDM are likely. This means that there is an 

increased potential for gaming. Researchers are beginning to address this issue. Ideally, the 

future NAS can be designed to preclude or at least minimize user gaming. 

We enhance PNP with supporting capabilities so that researchers can investigate this question. In 

particular, for any given future NAS, an important question is: What types of games are possible 

and what is their impact? Figure 2.1 illustrates the PNP architecture and how the AOC clients 

communicate with PNP. 

Probabilistic 

NAS Platform 

(PNP)

Weather Data

Reports

Graphical User Interface

Plan View Display

PNP API: Java Serialization/TCP Sockets

MATLAB®

Scripting

Interface

NAS

Database

ProbTFM DAC AOC1

Decision making NAS Simulation

Performance Data

Flight Data

AOC2 ... AOCN

Probabilistic 

NAS Platform 

(PNP)

Weather Data

Reports

Graphical User Interface

Plan View Display

PNP API: Java Serialization/TCP Sockets

MATLAB®

Scripting

Interface

NAS

Database

ProbTFM DAC AOC1

Decision making NAS Simulation

Performance Data

Flight Data

AOC2 ... AOCN

Figure 2.1 PNP architecture with AOC clients included. 
6 

Our solution approach begins with AOC clients that represent actual airline teams. They have 

access to all the traffic data, but can make TFM initiatives only for their own aircraft. The 

intention is not necessarily to represent all aircraft, so we do not necessarily require all flights to 

map to an AOC client. On the other hand, we can create a generic AOC client that handles all 

flights, using generic decision making logic, not handled by the other AOC clients. 

AOC clients may not alter flights that are scheduled to depart in the current time bin. Those 

flights may be altered only by the TFM client (ProbTFM in this case). Flights scheduled to 

depart in future time bins may be altered by the AOC clients. Therefore there are no direct 

interactions between the AOC and TFM clients. 

There of course may be indirect interactions between the AOC and TFM and DAC clients, as 

their respective actions alter the future system state on which the other clients will operate. 

2.2 Model functional requirements 

This section describes our functional requirements. These requirements support the modeling of 

future, hypothetical NextGen gaming actions. The functional requirements for our AOC models 

are as follows: 

1. The TFM client (ProbTFM) shall have access to data describing all AOC actions. 

2. The AOC clients shall implement the same algorithm to compute flight congestion cost, 

threshold and rankings that the TFM client normally does. This allows the AOC clients to 

forecast which of its flights are likely to be delayed or rerouted and take proactive actions 

accordingly. 

3. The AOC look-ahead time window (AOC_LAT_START and AOC_LAT_STOP) shall 

be user specifiable (default values are 4 and 1 time bins, respectively). Each AOC has its 

own AOC_LAT_START and AOC_LAT_STOP values. The specified start and stop bins 

specify which flights may be delayed or rerouted. Specifically, the look-ahead time 

window is obtained by adding AOC_LAT_START and AOC_LAT_STOP to the current 

time bin, to obtain two future time bins. Aircraft which are scheduled to depart within 

that time window (inclusive) are available for delay or reroute. For example, the 

AOC_LAT_STOP default value of 2 indicates that flights scheduled to depart in the 

second time bin immediately following the current time bin are available for delay or 

reroute. AOC_LAT_START must be greater than or equal to AOC_LAT_STOP. 

4. The AOCs shall be provided with the nominal TFM suggested delays and reroutes for 

any flight which the AOC may decide to delay or reroute. That is, the TFM procedure 

should run in a look-ahead mode in AOC, where it generates its delays and reroutes for 

future time bins (not the current time bin as it usually does). 

5. The AOCs will be supplied with, or be able to save, the history of the TFM suggested 

delays and reroutes for its flights. That is, in a given time bin, the AOC needs to know 

not only the current nominal TFM suggestions for a flight, but also the nominal TFM 

suggestion for the flight from previous time bins. 
7 

6. The AOC decision making logic shall be user specifiable. 

7. The AOC client actions and subsequent TFM action for each flight shall be logged and 

available for both post-processing purposes, and run-time purposes such as AOC 

learning. 

8. AOC clients shall have a specified scope. An AOC’s scope determines which aircraft it 

may control. The scope may be defined as a particular (real) airline, in which case the 

AOC may control only flights belonging to that airline, or the scope may be generic in 

which case the AOC may control all flights not otherwise under the control of a different 

AOC. 

9. The number of AOCs and their scope shall be user specifiable. 

10. The number of AOCs shall be variable from 0 to 20. 

2.3 Model design 

This section describes the AOC decision making logic. Note that the AOC module runs its own 

TFM procedure in a look-ahead mode that is independent of the TFM client. AOC clients only 

take proactive actions for future flights that will depart between AOC_LAT_START and 

AOC_LAT_STOP. Although this procedure is independent, the AOC uses the TFM algorithm 

(the algorithm implemented in ProbTFM in this case), giving the AOC insight into the likely 

TFM initiatives. 

2.3.1 AOC planning horizon parameters 

We use two planning horizon parameters. They are user specifiable and AOC specific. The AOC 

look-ahead time window, defined by the two parameters AOC_LAT_START and 

AOC_LAT_STOP, indicates the time window in which the AOC may implement delay or 

reroute for a flight. These values are specified in terms of the number of time bins from the 

current time bin. 

For example, suppose the current time bin is t. Then an AOC_LAT_START value of 4 indicates 

the AOC look-ahead time window begins in the t+4 time bin. Similarly, an AOC_LAT_STOP 

value of 1 indicates the AOC look-ahead time window stops in the t+1 time bin. Only flights of 

that airline with scheduled departure times that fall into that time window (inclusive) are 

considered by the AOC client for delays or reroutes. That is to say, in this example, the AOC is 

able to examine and make flight plan changes for flights that are scheduled to depart within the 

[t+1, t+4] time window. 

The default values are 4 and 1 for AOC_LAT_START and AOC_LAT_STOP, respectively. The 

maximum value is 6 for AOC_LAT_START. The minimum value is 1 for AOC_LAT_STOP. 

And AOC_LAT_START must be greater than or equal to AOC_LAT_STOP. 
8 

2.3.2 Distinguishing causes of high congestion cost 

A flight may have a high TFM cost because the NAS capacity elements it transits (sectors and 

the two airports) have forecasted reduced capacity (e.g., due to weather) or have forecasted high 

demand, or some combination of the two. Metrics can be computed to indicate the relative 

importance of capacity reduction versus demand increase, for a particular flight. We use our 

weighted capacity reduction to demand ratio metric. First, we define the capacity reduction to 

demand ratio for any given sector or airport, in a given 15 minute time bin: 

i

i

i D

CR CRD  (2.1) 

where 

CRDi = Capacity reduction to demand ratio for i-th element (airport or sector), 

CRi = Capacity reduction for i-th element (clear weather capacity – capacity 

Probability Mass Function (PMF) mode), 

Di = Demand for i-th element (demand PMF mode). 

The CR value is computed as the clear weather capacity minus the mode of the forecasted 

capacity PMF. The CRD metric is non-negative. A value of zero indicates that capacity reduction 

is not the cause of congestion cost. On the other hand, a value near unity indicates that both 

capacity reduction and demand are roughly equal causes of congestion cost. And a large positive 

value indicates that capacity reduction is the main cause of the congestion cost. We then compute 

the weighted capacity reduction to demand ratio metric, for a flight, as: 

i

N

i 1

WCRD   CRDi  ECC  (2.2) 

where 

 WCRD = The weighted capacity reduction to demand ratio for a flight, 

 ECCi = Congestion cost for i-th element (airport or sector, computed by the 

TFM algorithm). 

The WCRD is an aggregate CRD metric for a flight, weighted toward those elements that contain 

the most congestion cost for the flight. Some AOC decision schemes incorporate the WCRD

metric to help in deciding whether preemptive action should be taken. For instance, a large 

WCRD value suggests that inaction may be effective, since the congestion cost for the flight is 

caused by other flights which may deviate. On the other hand, a value near zero suggests that 

inaction will not be effective, since the congestion cost for the flight is caused by capacity 

reduction (e.g., due to weather), which likely will not improve. 

2.3.3 Overview of delay cost 

There are different types of cost that may be incurred as a result of flight delay at different levels. 

In practice, in order to maintain punctuality and predictability, the AOC adds minutes of buffer 

to their schedules. However, the term “cost of delay” in this EDD does not include these AOC 

strategic buffer delays. It refers only to the delay that is incurred on a day-to-day basis, where 
9 

they are measured against planned activities. We use term “primary delay cost” to represent the 

direct cost of delayed flight due to at-gate, taxi, en-route or holding delays. These may be caused 

by a number of factors, such as TFM decisions, AOC technical problems or weather change. 

And, the term “network delay cost” denotes the cost associated with the subsequent delays that 

are attributed to an initial primary delay. These delays may propagate throughout the entire 

network. 

An important factor in the AOC decision making process is the delay cost of each flight. Delay 

cost may be computed simply based on time and fuel. But for excessive delays (longer times), 

additional costs are incurred such as overtime costs and passengers missing connections. AOCs 

account for this type of non linear cost of delay and we consider four factors that contribute to it: 

aircraft type, delay magnitude and time of day, and schedule interactions. These factors are 

discussed in the following sections. 

The aircraft type determines the baseline operating cost that is primarily related to the fuel usage. 

We define four aircraft size categories: small (0-65 seats), medium (65-150 seats), large (150-

250) seats and super (>250 seats). We also define three delay duration categories: 0-15 minute 

delay, 16-64 minutes and more than 65-minute delay. For each delay duration, “low,” “base” and 

“high” cost scenarios have been calculated, to furnish a range of costs for the purposes of 

comparison. 

Table 2.1 Primary ground delay cost without network effect ($/min).

Tables 2.1 and 2.2 are derived delay cost statistics in dollars based on a linear interpolation of the 

original tables in study [4]. They provide ground and en-route statistics used in our current 

implementation but do not include any delays that are caused to future flights because of lack of 

aircraft or crew at another location (the network delay). 

For example, in a low cost scenario, a 10 minute ground delay of a small aircraft only costs 

$0.588×10, or $5.88 in terms of the primary delay cost. For the same cost scenario, a 20 minute 

ground delay of a large aircraft costs the AOC about $8.232×20, or $164.64. 
10 

Table 2.2 Primary en-route delay cost ($/min).

Delay magnitude and time of the delay. As with the baseline cost, the delay cost associated with 

network effects is expected to increase nonlinearly. Network effects are small for short delays of, 

say, less than five minutes because the main effect of this delay is on the direct operating cost 

due to the longer flight time and the airlines build some slack into their schedules to 

accommodate delay. 

Longer delays, on the other hand, can disrupt gate scheduling and ramp activities, disrupt crew 

scheduling and result in overtime labor rates. Importantly, longer delays can also cause later 

flights to be delayed due to lack of airframe or crew availability. These delays can propagate 

throughout the network until the end of the day. Figure 2.2 gives an example of the network 

effect. The data reveal both the time of day dependence and a length of delay dependence. Both 

of these factors influence the network delay (or ripple effect), where early flights impact 

subsequent flights and therefore increase overall cost of those delays. Data and analysis come as 

the courtesy of Dr. Steve Waslander and Mercer Management Consulting, 2003. 

Figure 2.2 Delay cost for a European low cost carrier for all flights from Oct 2000-Oct 2002. 
11 

The unit cost of the baseline delay serves as a benchmark for estimating the network delay cost. 

Our approach relies on a data-driven study [5], in which delay propagation was analyzed using 

actual American Airlines schedule data. We adopt their concept of a “delay multiplier” (DM) to 

quantify the magnitude of the network effect caused by the initial primary delay. The detailed 

DM numbers are provided in Fig. A.1 in the Appendix. For a baseline delay of 10 minutes at 

6:15 AM, the DM value is 1.62. So, approximately 1.62×10, or 16.2 minutes of total delay may 

be expected when the network effect is taken into account. As a result, a further 16.2–10, or 6.2 

minutes reactionary delay is generated in addition to the primary delay of 10 minutes. 

In order to quantify the price of these reactionary delays, we use the numbers in Table 2.3 as the 

average network cost of per minute delay. These values are computed by averaging the primary 

delay costs over a variety of aircraft types and converting to dollar values. They address the 

AOC’s expenses related to fuel, maintenance, agent penalty, ground handling, airport, 

aeronautical charges en-route ATC, and so forth. As a continuation of the previous example, 

when in a low cost scenario, the additional 6.2 minutes yield a cost of 6.2×1.76, or $10.94. As 

Table 2.3 indicates, network delays greater than 15 minutes would cause substantially escalated 

network cost. 

Table 2.3 Average cost for network effect ($/min).

Schedule interaction. To model the total network delay costs, we also consider another factor, the 

effect of “hubbing.” Flights are often tightly scheduled to squeeze more flying time out of each 

plane. The greatest impact of delay happens when it causes customers to miss connections or 

cause aircraft to miss their next operation. Thus, in a hub-and-spoke network, connecting flights 

to the hub should have higher delay costs due to this effect. We use the following empirical 

method to identify these inbound flights. 

In the preprocessing phase, the PNP server computes the unimpeded scheduled arrival time for 

each flight from the demand data. For a given AOC client and hub airport pair, we calculate the 

total number of scheduled arrivals and scheduled departures for each 30-minute time bin. 

Figure 2.3 provides an example of United Airlines and its main hub, Chicago O’Hare 

International Airport. 

We assume that a sharp increase in arrivals followed by a large number of departures indicates a 

connecting or banking event. These connections are likely to be tightly scheduled (within one 

hour) so that delays on inbound flights affect the carriers’ performance adversely and 

extensively. We identify the time windows with arrival banking immediately followed by 

departures (the yellow circles in Fig. 2.3 indicate these arrival banks). 

Not every flight that is scheduled to arrive in these periods, however, is an important inbound 

flight that needs to be on time. So we define an inbound flight be a flight that has more than 125 
12 

seats (in other words, larger than a B737 aircraft) and that is scheduled to arrive at the AOC’s 

hub in a yellow-circled time period. The AOC clients have the option to assign higher delay 

costs to these inbound flights, using a user-specifiable multiplier coefficient B based on the 

banking status of a departure. 

Figure 2.3 Schedule arrival and departure counts for United Airline-Chicago ORD airport for 

2007-8-30 with yellow circles indicating arrival banks. 

Costs of passenger delay. The passenger cost of delay reflects the costs that can be associated 

with the passenger compensation. More importantly, it is closely related to the opportunity cost 

of potential revenue loss due to future loss of market share as a result of lack of punctuality. 

Thus, it is an important consideration when calculating total delay costs. We use an average of 

$0.44 per passenger, per delay minute [4] and we assume that the total cost of passenger delay is 

proportional to the length of the delay, the aircraft capacity and the flight load factor. 

In our current cost model, frequency to that location is not considered. The mathematical formula 

to calculate the cost of passenger delay is: 

PassDelayCost  0.44CAPLOADBaselineDelay (2.3) 

where 

CAP = Number of aircraft seats, 

LOAD = Average aircraft loading, 

BaselineDelay = Amount of primary delay of the aircraft (min). 

The LOAD factor is a user-specifiable, AOC-specific parameter with a default value of 70%, 

80% and 90% in the low, base and high scenarios. So for an aircraft size of 100 seats with an 

aircraft load factor of 80%, a 10-minute primary delay incurs a total of 100×80%×10×0.44, or 

$352 in passenger delay cost. Note that this passenger delay cost is not an actual AOC cost. 
13 

Rather, this cost is merely used in the AOC decision making logic to avoid excessive passenger 

delays and potential market share loss. 

Model of AOCs total delay cost. In this section, we summarize the total cost of flight delay. We 

refer to this metric as the flight delay cost (FDC). It incorporates the cost of baseline delay, the 

network effects and potential market share loss previously defined. FDC, which in our context 

results from ground delay and/or pre-departure reroute, is a flight specific indicator, calculated 

by the AOC and whose value is generally private. Here is the definition of FDC. 

(1 InBd *B) BaseNetwor kCost PassDelayCost

FDC PrimaryDelayCost

  

 

 (2.4) 

where 

InBd = The weighted capacity reduction to demand ratio for a flight, 

B = Positive coefficients that are AOC specific and user specifiable with a 

default value of 1. 

If a flight is an inbound flight to a hub in a banking scenario, it will have additional cost on top 

of the base average delay cost at the network level. As a result, the AOC will be more reluctant 

to delay or reroute this type of flight. 

2.3.4 Comparison factors 

From the AOC’s perspective, comparisons with the major competitors may be an essential factor 

in their decision process. For example, for marketing purposes, they might be concerned with 

how delays are distributed between users. We know that mean delay plays an important role in 

AOC’s daily operation. However, of equal or greater importance is the timing and distribution of 

these delays. Consider, for example, a hub-and-spoke network. Here it would be better if all 

aircraft were to arrive and depart with the same (smaller) delay, rather than having just a few 

arrivals with a very long delay. So we consider two comparison factors, the mean comparison 

factor (MCF) and variance comparison factor (VCF), respectively. MCF indicates whether the 

delay is assigned proportionally between departing flights in a given time bin, and VCF reflects 

whether the uncertainty of delay is distributed evenly. For current time bin t, MCF is defined as: 

[j][t] [i][t]

[i][t] [i][t]

SD /NT

SD /NT

MCF[i]  (2.5) 

where 

SD[i][t] = Total delay suggested for AOC i’s flights in time bin t, 

NT[i][t] = Number of AOC i’s flights scheduled to depart in time bin t, 

SD[j][t] = Total delay suggested for competitor j’s flights in time bin t, 

NT[j][t] = Number of competitor j’s flights scheduled to depart in time bin t. 

The VCF metric is defined as: 
14 

VAR[j][t]

VAR[i][t] VCF[i]  (2.6) 

where 

VAR[i][t] = The variance of suggested delay for AOC i’s flight in time bin t, 

VAR[i][t] = The variance of suggested delay for competitor j’s flight in time bin t. 

Note that MCF and VCF are merely subjective factors. From the AOC perspective, MCF (or 

VCF) values of one or less indicates a favorable delay distribution. But MCF (or VCF) values of 

greater than one indicates an undesirable situation, in which the AOC client perceives too much 

delay or too much unpredictability. 

2.3.5 Classes of AOC decision making logic 

This section discusses three classes of AOC decision making logic which successively build on 

each other. These classes are motivated by previous investigations of AOC decision making in 

cooperative and competitive environments [6-7]. They are ordered in terms of sophistication, 

intent and user-gaming. The structure of each algorithm is given below. 

Cooperative. Cooperative decision making uses only solutions suggested by the TFM algorithm. 

The only decision is a yes/no decision for each flight, on whether or not to implement the 

suggested delay or reroute. Figure 2.4 summarizes this logic. 

Figure 2.4 Overview of the cooperative AOC decision-making logic. 

There are two sub types of cooperative decision making, where the time bin in which to accept 

the TFM suggestion is either (i) fixed or (ii) computed. 

In the fixed scheme, the TFM suggestion is accepted in a predefined time bin and is specified in 

terms of the number of time bins from the current time bin using the variable NBins_Depart. 

Suppose the current time is t, an NBins_Depart value of 2 means the TFM suggestion is used 

when, and only when the flight’s scheduled time of departure is in time bin t+2. For any change 

to the flight plan, NBins_Depart must fall within the AOC look-ahead time window. 

AOC _ LAT _ START  NBins _ Depart  AOC _ LAT _ STOP (2.7) 
15 

Figure 2.5 shows the process for this cooperative-fixed scheme. Note that flights that are early in 

the day, whose scheduled departure time bins are prior to the NBins_Depart time bin, are not 

considered by the AOC for delay or reroute. The default value of NBins_Depart equals 

AOC_LAT_STOP+1. 

Figure 2.5 Cooperative fixed-scheme AOC decision-making logic. 

In the computed scheme, the AOC determines when and if to accept the TFM suggestion. The 

nominal algorithm to be implemented is explained next and the flowchart is shown in Fig. 2.6. 

First define the congestion cost of flights computed by the TFM algorithm as flight congestion 

cost, FCC. FCC is the sum of element congestion cost (ECC) that the flight transits throughout 

the flight. 

1. In the earliest time bin in which a flight may be delayed or rerouted, no action is taken 

(i.e., the TFM suggestion is not accepted). 

2. In subsequent time bins, the TFM suggestion is ignored if the initial FCC (i.e., not 

including the TFM-suggested delay or reroute) is less than it was in the previous time bin, 

and otherwise accepted. 
16 

Figure 2.6 Cooperative Computed-scheme AOC decision-making logic. 

Competitive. This class of AOC decision-making procedure builds on the cooperative logic. In 

the competitive logic, the AOC either derives its own delays or reroutes (rather than using the 

TFM-supplied options) or it opts to either cooperate or ignore TFM suggestions based on some 

criterion. There are two sub types of competitive decision making, where the decision to accept 

the TFM suggestion is either (i) time-based or (ii) comparison-based. 

In time-based competitive decision making, the AOC investigates the possibility of delaying the 

flight less than is suggested by the TFM algorithm. The candidate delay values are in increments 

of 15 minutes (i.e., the equivalent of a time bin), ranging from 15 minutes (1 time bin) up to the 

greatest multiple of 15 that is less than the TFM suggested delay. At each new time bin t, given 

TFM suggested actions, the competitive algorithm selects the desired amount of delay. 

Multiplier, a user specifiable parameter, is used to compute the alternative delay. The delay value 

(picked by the AOC) equals the minimum between 15×Multiplier and the TFM suggested delay. 

The parameter Multiplier has a default value of 1. These alternative delays are designed in an 

attempt to reduce congestion costs, but only to a necessary magnitude. For instance, the TFMsuggested delay may be longer than needed. If the TFM suggestion is a reroute rather than a 

delay, then this type of decision-making simply accepts the reroute. Figure 2.7 summarizes this 

logic. 
17 

Figure 2.7 Time-based competitive AOC decision-making logic. 

Next is the comparison-based competitive logic. Based on the scheduled flight time, the AOC 

first classifies the TFM suggested flights into short-haul or long-haul categories. Flights having a 

flight time of less than 90 minutes are defined as short-haul and otherwise long-haul. For every 

flight category and given the current delay allocation, the AOC calculates the comparison metric 

MCF (VCF) defined in Eqs. 2.5 and 2.6. If the AOC believes the delay distribution has been 

unfavorable, then the AOC chooses no action and relies on other AOC clients to resolve the 

congestion problems. Otherwise the AOC selects the cooperative logic. Figure 2.8 describes this 

logic. 

Figure 2.8 Comparison-based competitive AOC decision-making logic. 

Strategic. In this class of decision making, the AOC clients can decide whether or not to 

cooperate and whether or not to derive their own delays or reroutes. Furthermore, they can 

choose alternative flights for delay other than the ones suggested by the TFM algorithm, or even 
18 

choose alternative solutions other than TFM suggestions. Under this framework, the alternatives 

are generated not only to reduce congestion cost, but also to consider other traffic that is not 

under the control of the AOC, and also in a way to integrate the AOC’s economic model. 

Specifically, we consider three types of behavior in this strategic class. 

In the first type of strategic logic, the parameter WCRD (see Eq. 2.2) is used to decide on action 

versus inaction. When WCRD is greater than unity, then the AOC takes no action on the flight. 

Instead, the AOC realizes that other flights may delay or reroute, thus solving the congestion 

problem. On the other hand, when WCRD is less than or equal to unity, then the AOC uses the 

strategies in the competitive framework for this flight. The logic is summarized in Fig 2.9. 

Figure 2.9 First-type strategic AOC decision-making using WCRD. 

The second type of strategic behavior can be characterized as follows: the AOC computes the 

weighted difference between the flight ground delay cost FDC and the flight congestion cost 

(FCC) in order to decide which flight to delay. We borrow the economic term “surplus” to 

denote this difference. 

Surplus  FDC(FRC)  FCC  M (2.8) 

Parameter M is an AOC-specific user specifiable multiplier, ranging from 0 to 1. It reflects the 

relative AOC decision making preference between reducing the congestion it produces and 

minimizing its own delay cost. The default value of M is 1. 

The value of surplus is applied as a rescoring rule to rank all the candidate flights (including the 

initial ones suggested by the TFM algorithm). Flights with higher FDC have higher surplus 

values and should be less likely to be chosen for delay or reroute by the AOC client. Given the 

FDC, AOC clients are more willing to delay flights having higher FCC values. Thus, AOC 

clients will sort the flights in accordance with the computed surplus value. Then the AOC will 

pick a certain number of flights for delay or reroute, starting with the one that has the minimum 

surplus value. Figure 2.10 illustrates this logic. 
19 

Figure 2.10 Second-type strategic AOC decision-making logic using Surplus. 

In the third type of strategic behavior we continue to use AOC_LAT_STOP and 

AOC_LAT_START to specify the time window in which the AOC evaluates the TFM 

suggestions. But we use the additional parameter N_Element_Look to specify the number of 

NAS elements (airport and sectors) to search when the AOC client tries to find the most 

congested elements of the TFM suggested flight. 

This strategy also uses TOT_Alt_Flights to define the total number of alternative flights to be 

selected. For example, a TOT_Alt_Flights value of 10 and an N_Element_Look value of 3 

means that the AOC will examine the top 3 congested elements related to the flight being 

evaluated. The AOC first searches for flights that transit all three of them. The AOC next 

searches for flights that transit two of them, and then finally flights that transit one of them until 

the total number of flights found sums up to 10. It is possible that the AOC client will find less 

than 10 at the end of the search. Both N_Element_Look and TOT_Alt_Flights are AOC-specific 

and user specifiable. The default value of N_Element_Look is 3 and TOT_Alt_Flight is 10. 

The incentive of the third type of AOC strategic behavior is to find alternative solutions with 

respect to user preferences. In this type of decision logic, the AOC computes the flight delay cost 

(FDC) for both the ground delay and reroute options for a flight that is suggested for ground 

delay (or reroute) by the service provider. If the ground delay cost exceeds the DVR multiplied 

by the reroute delay cost, then the AOC chooses to reroute the flight. Otherwise, the AOC 

accepts the ground delay. The solution AOC chooses might be the opposite of the TFM 

suggested action. 
20 

2.3.6 TFM decision making logic 

Depending on the AOC actions, congestion may or may not be resolved. For flights departing in 

the current time bin, the decision-making process for the TFM client is to use ProbTFM to 

resolve any residual congestion. 

AOC clients can take proactive actions to reduce congestion in lieu of responding to congestion 

when it happens. However, it is also likely that AOC clients desire to minimize their own 

response and to let their competitors resolve the congestion problem. Furthermore, an AOC 

client may even intentionally jeopardize competitors’ flight plans. The TFM client should be 

aware of these possibilities, and use appropriate responses to penalize undesirable decisions that 

will lead to a degraded system. 

To penalize these undesirable AOC actions, we modify the flight congestion cost. We use a 

penalty function to increase the flight congestion cost for flights belonging to AOCs that have 

taken undesirable actions in the recent time bins. The penalty function constitutes to a new, 

adjusted congestion cost (ACC) for flight f (scheduled to depart in time bin t) in the following 

way: 

     





 ACC FCC (FCC Congestion Threshold )

| N[t 1] |

k 1

t 1

k

t

f

t

f (2.9) 

10 %,i AOC } CongestionThreshold

FCC CongestioThreshold N[t 1] { i | i

t 1

i     



 (2.10) 

The congestion threshold is a predefined cut-off value determined by the TFM service provider. 

The first term in the right-hand side of Eq. 2.9 is the flight congestion cost computed by 

ProbTFM by aggregating congestion cost of sectors and airports flight f will transit. The second 

term is the penalty part that is the sum of excess congestion from the previous time bin. 

The user-specifiable parameter  represents the percentage increase in the congestion cost due 

to a penalty. The default value of  is set to be 1. 

N [t–1] is a set containing all the flights that belong to AOC i and that departed in the previous 

time bin t–1 and exceeded the congestion threshold by more than 10%. |N [t–1] | is the 

cardinality of the set. 

The procedure taken by the TFM decision logic is as follows: 

1. For the previous time bin (t–1), for each AOC client, select departed flights with a FCC 

value that was 10% above the congestion threshold. 

2. Among the selected flights, aggregate the excess congestion for the time bin t–1 and 

compute the adjusted congestion cost (ACC) according to Eq. 2.9. 
21 

3. Identify the flights to be delayed or reroute with respect to ACC values and the 

congestion threshold specified. 

Compliant AOCs, which ensure that their high congestion cost flights remain below the 

congestion threshold, incur no such penalties as described above. Therefore, this decisionmaking penalty implemented in ProbTFM does not introduce any strange TFM decisions in these 

compliant cases. 

3 Investigation of Impact of Gaming Strategies 

In this section we describe results of NAS gaming simulation experiments using the user gaming 

behaviors described in Section 2. 

3.1 Experiment outline 

This section discusses the experiment scenario, demand structure and design. 

3.1.1 Scenario 

For this experiment, we use Nov.16 (Thursday), 2006. The NAS traffic volume was relatively 

high, with 58,259 instrument flights. As Fig. 3.1 shows, the NAS weather was severe with a 

round of late season storms affecting the Midwest and Northeast corridor. 

Figure 3.1 Nexrad national mosaic reflectivity image from November 16, 2006. 

3.1.2 Demand structure 

The demand set contains information on flight plan, including flight number, aircraft type, 

departure and arrival airport, and scheduled departure time. We consider in total 10 commercial 
22 

carriers (listed in Table 3.1, column 1). They represent 30-40% of the total NAS operations in 

this demand data. AAL (American Airline) and EGF (American eagle) are represented as one 

carrier. This is because their operations are jointly scheduled regularly regardless of their 

separate flight plans. Similarly we also merge COA with BTA, and ACA with JZA. 

Column 2 in Table 3.1 specifies the hub airports being considered for every commercial carrier. 

In column 3, we provide a list of the time windows we determined to represent banking events of 

the corresponding inbound flights. They are denoted by 15-minute time bins. 

Table 3.1 Scheduled arrival time for inbound flight (ICAO code).

Carrier Hub 

Apt 

15-minute Time Bins (Inbound Flight Scheduled Arrival ) 

SKW DEN 27,28, 35,36,41,42,47,48,55,56,63,64,69,70 

ORD 15,16,17,18,25,26,45,46,53,54,55,56,63,64,65,66 

LAX 25,26,69,70,73,74,79,80 

FLG MEM 19,20,37,38,57,58 

DTW 13,14,33,34,39,40,47,48,61,62 

NWA MSP 19,20,25,26,27,28,35,36,43,44,49,50,57,58,71,72 

DTW 19,20,27,28,33,34,39,40,45,46,53,54,61,62,67,68 

MEM 17,18,19,20,39,40,57,58,59,60 

UAL DEN 21,22,31,32,45,46,47,48,61,62,65,66,71,72 

ORD 15,16,35,36,45,46,49,50, 53,54,61,62,65,66,71,72 

IAD 45,46,63,64,65,66 

SFO 33,34,43,44,53,54 

DAL ATL 13,14,19,20,31,32,39,40,41,42,43,44,47,48,53,54,59,60,61,62 

SLC 29,30,53,54,67,68,71,72 

CVG 15,16,39,40,57,58,61,62 

USA CLT 17,18,25,26,31,32,37,38,43,44,49,50,57,58,65,66,67,68 

PHL 17,18,33,34,41,42,49,50,59,60 
23 

SWA MDW 17,18,21,22,23,24,27,28,33,34,43,44,53,54,63,64,69,70 

LAS 29,30,33,34,42,44,59,60,65,66,73,74 

PHX 25,26,33,34,55,56,61,62,67,68,77,78 

HOU 25,26,37,38,49,50,53,54,65,66 

AAL 

EGF 

DFW 19,20,21,22,23,24,27,28,37,38,39,40,45,46,51,52,55,56,59,60,63,64,71

,72 

ORD 15,16,19,20,27,28,33,34,37,38,47,48,59,60,65,66 

COA 

BTA 

IAH 19,20,27,28,43,44,57,58,65,66 

EWR 13,14,15,16,37,38,39,40,55,56,57,58,63,64 

CLE 15,16,17,18,25,26,31,32,41,42,47,48,55,56,61,62 

ACA 

JZA 

YYZ 15,16,27,28,33,34,43,44,49,50,57,58,67,68 

YUL 11,12,33,34,35,36,47,48,57,58 

YVR 23,24,35,36,37,38,55,56,67,68 

YYC 33,34,35,36,57,58 

In our experiments we implement two AOC groups. These groups consist of different 

commercial carriers. The first AOC group, which we label as “AAL,” is comprised of American 

Airline, American eagle, Continental and Northwest Airline. The second AOC group, which we 

label as “UAL,” includes United Airline, US Airways, Southwest, SkyWest and Air Canada. 

These two AOC teams account for roughly 33% of the total NAS-wide operation in this demand 

data. These groupings were selected to reflect on-going strategic alliances. United, Air Canada 

and US Airway are often teamed up while Continental, Delta and Northwest are also often 

teamed in flight scheduling and code shares. 

3.1.3 Experiment design 

For computational efficiency we implement a distributed server-client architecture that includes 

three PCs, as shown in Fig. 3.2. Most of our experiments involve 10 commercial airlines which 

we divide into the two airline groups described in Section 3.1.2. 
24 

Figure 3.2 Simulation server-client architecture configuration. 

The goal of this experiment is to gain insights on consequence of distributed TFM decision 

making in terms of overall NAS performance. Therefore we first make a baseline simulation run 

in which no gaming strategies are used. We next implement single-AOC gaming strategies, 

followed by simulation runs in which both AOC groups use the same strategies, and finally 

simulation runs in which the two AOC groups use different strategies. 

3.2 Gaming strategies 

This section summarizes the gaming strategies simulated in our experiment. See Section 2.3 for 

the algorithmic details of these strategies. As Fig. 3.3 illustrates, we experiment with four 

different gaming strategies. 

Figure 3.3 The four different gaming strategies fall into three categories. 

The fixed cooperative strategy uses only TFM solutions that are planned, but not yet imposed, by 

the TFM service provider. Therefore, the only decision this strategy makes is whether or not to 

implement the suggested delay or reroute. Users accept the initiative in order to lock it in, and 
25 

avoid possible additional delays that may be added in the TFM planning phase. Users reject the 

initiative in order to avoid taking an unnecessary delay that will be reduced in the planning 

phase. Figure 3.4 illustrates this strategy. 

Figure 3.4 The cooperative-fixed strategy. 

In time-based competitive strategy, the AOC derives its own initiative rather than using the 

TFM-supplied options. In this case the AOC’s initiative is tailored to its business model. By 

implementing its own TFM initiative, the user hopes to avoid the service provider solution which 

may have longer delay, or otherwise create greater delay cost because it fails to account for the 

user’s business model. Figure 3.5 illustrates this strategy. 

Figure 3.5 The competitive time-based strategy. 

Strategic strategies take flights of other users into account. In the WCRD strategy, the AOC 

considers the likely cause of the forecasted congestion, which is creating the need for a pending 

delay or reroute. For instance, a pending delay or reroute may be caused by heavy demand. On 

the other hand, the TFM initiative may be due to capacity reductions, caused by various types of 

inclement weather. 

While the service provider and the users cannot change the weather, they can manage the 

demand profile with delays and reroutes. This WCRD strategy exploits this by implementing the 

cooperative-fixed strategy when capacity reduction is the source of the forecasted congestion, 

but ignoring pending delay or reroutes when heavy demand is the problem. In other words, this 

strategy holds out on implementing a TFM initiative when it is possible that other AOCs may 

resolve the congestion problem. Figure 3.6 illustrates this strategy. 
26 

Cooperative Logic

N

Figure 3.6 The strategic WCRD strategy. 

In the strategic-surplus strategy, the users search for lower cost flights which it can delay to 

avoid delays on its higher cost flights. By evaluating the demand of other users along with its 

own demand, the user computes the forecasted congestion. The user next determines the flight 

congestion cost of its own flights. That is, it determines which of its own flights can best reduce 

congestion by delay or reroute. The user then computes a weighted difference between the flight 

delay cost and the flight congestion cost in order to find the best flights to delay or reroute. 

Figure 3.7 illustrates this strategy. 

Figure 3.7 The strategic surplus strategy. 

3.3 Results 

This section discusses the results of our user gaming experiments. For our simulated day, we 

divide the flight delays into three categories: the AAL group, the UAL group, and the other 

flights which do not follow any gaming strategy. And we compare these delays, under various 

experimental conditions, to the baseline case where no strategies are used. 

3.3.1 Single AOC results 

We begin with the less complex case of a single AOC (the AAL group) using gaming strategies 

while the UAL group joins the non AOC flights in merely adhering to the TFM service 

provider’s initiatives. Figure 3.8 shows such results in the case of medium TFM activity. At this 

medium level of TFM activity the NAS wide delay is about 15-20 minutes per flight which is 

reasonable for this heavy weather and heavy traffic scenario. 
27 

In addition to the baseline case, Fig. 3.8 shows results for five experiments where the two AOCs 

use the same strategy. Our five experiments use the four different strategies described in 

Section 3.2. The competitive strategy is run twice using a 15 minute and 30 minute look-ahead 

time (LAT) horizon, for a total of five cases. 

Figure 3.8 Gaming experiment results for a single AOC using the same strategy. 

In the baseline run where no gaming strategies are used by any users, the UAL group has 

significantly greater delay than the non AOC flights (20 minutes per flight compared to 16.5 

minutes per flight, respectively). This indicates that the UAL group has a greater fraction of 

flights involved in congested airspace and airports. This congestion is due to some combination 

of reduced capacity due to weather impacts and high demand. In any case, the UAL group has a 

demand structure that differs considerably from that of the non AOC flights. 

Likewise, when the AAL group engages in gaming behavior, the impact on the UAL group 

differs from that on the non AOC flights. As Fig. 3.8 shows, The UAL group flights experience 

.5 to 1.0 minutes of delay per flight increase when the AAL group uses the competitive-15 and 

the cooperative strategies, respectively. Otherwise the UAL group flights experience little overall 

delay change when the AAL group uses any of the other three strategies. 

The non AOC flights, on the other hand, experience 1.5 to 2.5 minutes of delay per flight 

increase for all AAL group strategies except the strategic-surplus strategy. 

The AAL group flights experienced .5 and .9 minutes of delay per flight decrease when they use 

the competitive-15 and the strategic-surplus strategies, respectively. Otherwise they experience 

little overall change, though particular flights were improved. Gaming actions do not necessarily 

improve the overall delay primarily for two reasons. 

First, just as a gaming action can cause flights outside the AOC to be delays, so too such actions 

can have the unintended consequences of delaying own flights. In such case these unintended 

consequences can be difficult to predict since the NAS delay and rerouting decisions are 

complex and can have far reaching implications. Delaying or cancelling even a single flight can 

have implications that persist for several hours later [3]. 
28 

Second, gaming strategies that focus on the flight delay cost may reduce the overall fleet delay 

cost but not the overall fleet delay. 

3.3.2 Dual AOC results 

We next examine the more complex case of two AOC groups using gaming strategies. We begin 

with both groups using the same strategy. We repeat the Fig. 3.8 experiments, except that the 

UAL groups use the same strategies that the AAL group uses. Figure 3.9 shows these results, 

again in the case of medium TFM activity. 

Figure 3.9 Gaming experiment results for two AOCs using the same strategy. 

Compared to the Fig. 3.8 experiments, when the UAL group implements gaming strategies its 

delay consistently improves. Only in the competitive-30 does its overall delay remain 

unchanged. In all four of the other strategies its delay reduces, anywhere from a fraction of a 

minute per flight to almost two minutes per flight. 

On the other hand, the AAL group delays mostly degrade, illustrating the impact of the gaming 

actions of flights outside the AOC. Interestingly, the competitive-15 delay does improve for the 

AAL group (as well as for the non AOC and the UAL group). 

But the non AOC flights degrade significantly for the cooperative, competitive-30, and strategicWCRD. In these cases the non AOC flights delay increases by 2.8, 2.7 and 2.0 minutes per 

flight, respectively. 

These degradations in the AAL group and in the non AOC flights, when the UAL group 

implements several of the gaming strategies, suggests a race to the bottom scenario, where the 

NAS evolves to levels of decreasing performance as users activate gaming strategies. For 

instance, consider the scenario where the AAL group implements the strategic-WCRD strategy 

and the UAL group then responds in kind. The AAL group’s initial action increased delay in the 

non AOC flights by 85k minutes. The UAL group’s response then increased the AAL group 

flights by 15k minutes and increased the non AOC flights by an additional 75k minutes, for a 

total of 90k additional minutes of delay. In other words, the AAL group’s initial action added 

85k minutes of delay, and the UAL group’s response then added an additional 90k minutes of 

delay, for a total of 175k minutes of total delay added due to the two gaming actions. 
29 

Given the two-AOC results in Fig. 3.9, the results of the cooperative, competitive-15 and 

strategic-surplus are straightforward. In all of these cases, where both the AAL and UAL groups 

use identical strategies, we find improvement in own flight and degradation in the non AOC 

flights. That is, the delay decreases for users following these strategies, and the delay increases 

for users not following any strategy. 

The delay reductions were most significant in the competitive-15 strategy, with just over one 

minute per flight delay reduction. More significant was the delay increase for the non gaming 

flights, particularly in the cooperative case. While in the competitive-15 and strategic-surplus 

cases the non gaming flights experienced only minor delay increases (less than 0.3 minutes per 

flight), in the cooperative case the non gaming flights experienced a delay increase of more than 

4.5 minutes per flight. 

These results also demonstrate the differences and potential interactions between the AAL and 

UAL groups. For the UAL group, any of the five strategies improved the overall delay, with the 

competitive-15 providing the best delay reduction. 

But in the competitive-30 and the strategic-WCRD cases, the AAL group experienced delay 

increases. This indicates interaction between the AAL and UAL groups, and that the UAL group 

was able to achieve delay reduction at the cost of delay in the AAL group. In both cases it is a 

net, NAS-wide, loss. That is, the AAL total delay increase exceeds the UAL delay decrease by 

about 20,000 and 40,000 minutes for the strategic-WCRD and competitive-30 cases, 

respectively. 

This degradation in NAS performance is substantially greater when the non AOC flights are 

included. These flights experience an additional 175k minutes of delay increase. These results 

indicate the serious performance degradation that can result from all three types of gaming 

strategies (that is, the cooperative, competitive-30 and strategic-WCRD strategies). For these 

three gaming strategies, the total NAS-wide delay increase is about 175k, 230k and 175k 

minutes, respectively, or about 3-4 minutes per flight. 

The competitive-15 and strategic-surplus strategies, on the other hand, are more benign in twoAOC cases. As noted above, in both cases the user benefits and non AOC flights experience 

delay increase, but the increases are minor. Indeed, in both cases the net NAS delay is reduced. 

The total delay reduction is about 6k and 15k minutes for the competitive-15 and strategicsurplus strategies, respectively. These translate into NAS-wide delay reductions of about .1 and 

.25 minutes per flight, respectively. 

These results suggest the following distinction between gaming strategies. First, in principle a 

strategy may improve not only own performance but other flights as well. We refer to this as an 

egalitarian type of strategy. None of our strategies exhibit this type of performance, and such a 

strategy would not qualify as a gaming strategy under our definition. 

Second while a strategy improves own performance, it may also improve other flights or its 

degradation of other flights is sufficiently minor that the net, NAS-wide, performance improves. 

We refer to this as a utilitarian type of strategy. 
30 

Third, a strategy’s degradation of other flights may be significant such that the net, NAS-wide, 

performance degrades. We refer to this as an aggressor type of strategy. This particular 

experiment demonstrates that the competitive-15 and strategic-surplus strategies can play 

utilitarian roles whereas the cooperative, competitive-30 and strategic-WCRD strategies can play 

aggressors roles. 

3.3.3 Impact of gaming strategies on system congestion 

In addition to the impact that gaming strategies have on the delay of own flights, flights of other 

AOCs, and non AOC flights, such strategies also impact airspace and airport demand time 

histories. In our experiments overloading of airspace and airports occurs when the loading, as 

measured in 15-minute time intervals, exceeds the weather-impacted capacity. Such overloading 

occurs in the NAS, and controllers can temporarily modify sector capacity by up to three 

operations to accommodate demand fluctuations. Also, regional and tactical flow initiatives, such 

as path stretching S-turns, help to smooth these fluctuations. 

Therefore our congestion metric is a measure of the service provider workload necessary to 

accommodate such overloading. Gate delays and strategic reroutes are used to manage such 

overloading, but these strategic initiatives do not replace the regional and tactical flow initiatives, 

and should not be used to eliminate all possible congestion. Such a zero-tolerance policy is too 

conservative. It would escalate delay to unreasonable levels and is not representative of NAS 

operations. 

Our congestion metric indicates the effectiveness of the gate delays and strategic reroutes and 

any mitigating factors, such as the gaming strategies present in these experiments. As Fig. 3.10 

shows, our gaming experiments generally increase the airspace congestion. 

Figure 3.10 NAS airspace congestion (#operations) for the single and dual AOC experiments. 

The Fig. 3.10 results show that while the cooperative strategy does not increase airspace 

congestion, the other four strategies do increase congestion by 200 operations over the course of 
31 

the day. Airspace congestion occurs over hundreds of different sectors and several hours, and so 

tens of 15-minute time intervals. This means that the additional 200 operations fall into 

thousands of sector-time intervals. 

Therefore this congestion increase is not a major operational impact. Nonetheless, it is an impact 

of the gaming strategies and reveals that such gaming scenarios involve a third “player.” In 

addition to the two AOCs (or more generally N AOCs), there is also the service provider that 

may be impacted by the shifting of the demand profile and thus increase in congestion. That is, 

NAS-wide user gaming can be viewed as a third-party game: the participating airlines, nonparticipating airlines and the service provider. Of course such user-induced congestion is 

possible only to the extent that users can influence the strategic TFM initiatives. 

3.3.4 Time histories of gaming strategy impacts 

As is typical, in our November 16, 2006 scenario the weather is heaviest in the afternoon and 

early evening. Likewise the congestion and delay are heaviest in these time periods. In this 

section we examine the delay time histories of AAL flights, UAL flights and the non AOC 

flights for four different gaming strategies which the AAL group and UAL group both use. 

Figures 3.11, 3.12 and 3.13 show the time histories for the strategic-surplus strategy. 

Figure 3.11 Delay time histories for AAL flights (not the entire AAL group) for both baseline and 

strategic-surplus cases. In the latter, both the AAL and UAL groups implement the 

strategy. 

Figure 3.11 shows that the AAL flights experience an 8.6% delay reduction compared to the 

baseline case. The delay reduction appears throughout the day, roughly proportional to the 

baseline delay. 
32 

Figure 3.12 Delay time histories for UAL flights (not the entire UAL group) for both baseline and 

strategic-surplus cases. In the latter, both the AAL and UAL groups implement the 

strategy. 

Figure 3.12 shows that the UAL flights experience a 6.5% delay reduction compared to the 

baseline case. As with the AAL flights, the delay reduction appears throughout the day, roughly 

proportional to the baseline delay, such that the majority of the delay reduction is achieved 

during the peak hours. The surplus strategy intelligently helps the airlines to find reasonable lowcost substitutes for important flights that are originally suggested for delays. This strategy shows 

its values during the peak hours. 

Figure 3.13 Delay time histories for the non AOC flights for both baseline and strategic-surplus 

cases. In the latter, both the AAL and UAL groups implement the strategy. 
33 

Figure 3.13 shows that the non AOC flights experience a 1.1% delay increase compared to the 

baseline case. During the peak hours these flights experience a slight delay reduction, but it is 

offset by delay increases late in the day. 

Figures 3.14, 3.15 and 3.16 show the time histories for the strategic-WCRD strategy. 

Figure 3.14 Delay time histories for AAL flights (not the entire AAL group) for both baseline and 

strategic-WCRD cases. In the latter, both the AAL and UAL groups implement the 

strategy. 

Figure 3.15 Delay time histories for UAL flights (not the entire UAL group) for both baseline and 

strategic-WCRD cases. In the latter, both the AAL and UAL groups implement the 

strategy. 
34 

Figure 3.14 shows that the AAL flights experience a 5.0% delay increase compared to the 

baseline case. The delay increase appears mainly at the peak delay period and at the end of the 

day. 

Figure 3.15 shows that the UAL flights experience a 6.1% delay reduction compared to the 

baseline case. The delay reduction does not occur during the peak delay period but in the 

following hours. 

Figure 3.16 Delay time histories for the non AOC flights for both baseline and strategic-WCRD 

cases. In the latter, both the AAL and UAL groups implement the strategy. 

Figure 3.16 shows that the non AOC flights experience a significant 20% delay increase 

compared to the baseline case. Some of this delay increase occurred during the peak delay 

period, but as with the UAL flights, the bulk of the delay difference (in this case delay increase) 

occurred later in the day. The fact that the UAL delay reduction slightly precedes the significant 

delay increase of the non AOC flights (evident in Figs. 3.15 and 3.16) suggests a causal 

relationship, as the UAL flight delay reductions are likely causing significant delays in the non 

AOC flights. Results such as these would serve as strong motivation for non gaming users to 

implement gaming strategies. 

Figures 3.17, 3.18 and 3.19 show the time histories for the cooperative strategy. 
35 

Figure 3.17 Delay time histories for AAL flights (not the entire AAL group) for both baseline and 

cooperative cases. In the latter, both the AAL and UAL groups implement the 

strategy. 

Figure 3.18 Delay time histories for UAL flights (not the entire UAL group) for both baseline and 

cooperative cases. In the latter, both the AAL and UAL groups implement the 

strategy. 

Figure 3.17 shows that the AAL flights experience a 3.1% delay reduction compared to the 

baseline case. The delay reduction appears mainly toward the end of the day. 
36 

Figure 3.18 shows that the UAL flights experience a marginal delay reduction. These flights do 

experience a significant delay reduction during the peak delay period, but it is offset by delay 

increases later in the day, possibly caused by the AAL group gaming actions. 

Figure 3.19 Delay time histories for the non AOC flights for both baseline and cooperative cases. 

In the latter, both the AAL and UAL groups implement the strategy. 

As with the strategic-WCRD case, Fig. 3.19 shows that the non AOC flights experience a 

significant 27% delay increase compared to the baseline case. And again, while some of this 

delay increase occurred during the peak delay period, along with the UAL flights, the bulk of the 

delay increase occurred later in the day. Perhaps the AAL group actions caused this and the UAL 

flight delays. In any case, the non AOC flights significant delay was caused by relatively small 

delay savings elsewhere. 

Figures 3.20, 3.21 and 3.22 show the time histories for the competitive-15 strategy. 

Figure 3.20 shows that the AAL flights experience a 7.8% delay reduction compared to the 

baseline case. The delay reduction appears both during the peak delay period and toward the end 

of the day. 
37 

Figure 3.20 Delay time histories for AAL flights (not the entire AAL group) for both baseline and 

competitive-15 cases. In the latter, both the AAL and UAL groups implement the 

strategy. 

Figure 3.21 Delay time histories for UAL flights (not the entire UAL group) for both baseline and 

competitive-15 cases. In the latter, both the AAL and UAL groups implement the 

strategy. 

Figure 3.21 shows that the UAL flights experience a 9.3% delay reduction compared to the 

baseline case. These flights do experience a significant delay reduction during the peak delay 

period and even more so in the immediately following period. 
38 

Figure 3.22 Delay time histories for the non AOC flights for both baseline and competitive-15 

cases. In the latter, both the AAL and UAL groups implement the strategy. 

Figure 3.22 shows that the non AOC flights experience only a slight delay increase (1.3%). 

Interestingly these flights experience, along with the AAL and UAL flights, a delay decrease 

during the peak delay period. This is offset, however, by a delay increase later in the day. Unlike 

the other strategies, the competitive-15 is more benign from the perspective of the non AOC 

flights. 

Clearly strategies such as the competitive-15 are desirable, although Fig. 3.8 shows that even this 

is not a consistent finding as the non AOC flights incur delay increase when only the AAL group 

implements the competitive-15 strategy. Obviously there is not a linear trend in performance as 

increasing numbers of flights adopt gaming strategies. 

Strategy Participating

Airlines

Non participating

airlines

Service provider

Cooperative Preferred Harmful No change

Strategic‐surplus Preferred Acceptable Slight workload

Strategic‐wcrd Some preferred Harmful Slight workload

Competitive (15 min) Preferred Acceptable Slight workload

Competitive (30 min) Some preferred Harmful Slight workload

Strategy Participating

Airlines

Non participating

airlines

Service provider

Cooperative Preferred Harmful No change

Strategic‐surplus Preferred Acceptable Slight workload

Strategic‐wcrd Some preferred Harmful Slight workload

Competitive (15 min) Preferred Acceptable Slight workload

Competitive (30 min) Some preferred Harmful Slight workload

Figure 3.23 Summary of findings for the dual AOC experiment. 

Figure 3.23 summarizes our findings for the implications when both the AAL and UAL groups 

implement a strategy. 
39 

3.3.3 Heterogeneous gaming strategies 

We next examine heterogeneous gaming strategies in which the AAL and UAL groups 

implement different strategies. Figure 3.24 shows the results of two such experiments, along with 

the baseline case and a single-AOC case for comparison. 

Figure 3.24 Gaming experiment results for two AOCs using different strategies. 

The Fig. 3.24 results show that both heterogeneous gaming experiments the AAL group flights 

incurred increased delay whereas the UAL group flights experienced a delay reduction in the 

case were the AAL group used the strategic-surplus strategy and the UAL group used the 

cooperative strategy. As with the AAL group, the non AOC flights also incurred increased delay 

in both experiments. 

Neither of the experiments in Fig. 3.24 improved the delay for both the AAL and UAL groups. 

We note, however, that in the case were the AAL group used the strategic-surplus strategy and 

the UAL group used the strategic-WCRD, while the UAL group incurred delay, the AAL group 

and the non AOC flights, incurred significantly greater delay. This raises the specter of a gaming 

strategy which seeks to harm performance of other flights, even at the cost of harming one’s own 

flights, but to a lesser degree. We refer to this as a spoiler type of strategy and it easy to see how 

it could contribute to a race to the bottom dynamic, discussed in Section 3.3.2. 

We note that in this particular case, the UAL group spoiler strategy depends on the strategy in 

use by the other users. This suggests that dynamic strategies could be designed to react to the 

strategies currently in use by the other AOCs. And of course sophisticated users would likely 

implement more complex hybrid strategies which switch between a set of strategies depending 

on the tactical scenario. 
40 

Figure 3.25 Gaming experiment results for two AOCs using different strategies. 

Figure 3.25 shows the results from two more heterogeneous gaming strategies. As with the 

Fig. 3.24 results, these results do not show consistent delay reduction for the AOC groups. We 

again note, however, that these results suggest a possible race to the bottom scenario. In this 

case, if the AAL group first implements the strategic-WCRD strategy then it could be 

advantageous for the UAL group to counter with the cooperative strategy. As Figs. 3.8 and 3.25 

show, such a counter move significantly increases delay for all other flights (AAL group and non 

AOC flights) while the UAL group delay remains unchanged. 

4 Conclusions and Recommendations 

This section presents the conclusions and recommendations of this effort. This section repeats 

the conclusions and recommendations given in the Executive Summary Sections 1.1 and 1.2. 

4.1 Conclusions 

We have developed models of NAS user gaming strategies to evaluate possible gaming actions 

and consequences in a future environment of increased collaboration, real time information 

sharing, and user influence on traffic flow management decision making. We have experimented 

with these models in our PNP NAS-wide simulation. Our conclusions are: 

Significant performance variations in gaming scenarios. Our results suggest that NextGen 

gaming does not reduce to a few principles. Several different gaming strategies can be 

envisioned, and each strategy has its own variants. For instance, the competitive-15 versus 

competitive-30 strategies shows significant differences, with the 15 minute time horizon showing 

much better performance. Furthermore, different users can select from different strategies, and 

very likely will use multiple strategies simultaneously in different scenarios. These many 

possible combinations make for an enormous problem space. In our investigation so far of this 

problem, we find that the performance of the different user groups can vary dramatically along 
41 

these dimensions. Slight modifications in a strategy (such as the competitive strategy look ahead 

time, see below), or a relatively minor increase in the number of users implementing a strategy 

can significantly alter results. Also, when different users implement the same strategy, one user 

may experience delay reduction while the other experiences a delay increase. Their different 

flight schedules, for instance, can cause such different results. We experimented with limited 

numbers of gaming users and strategies, but even in that more limited problem space found 

significant performance variations. While some trends are evident, our results suggest the 

NextGen gaming problem is not easily generalized into a relatively few, simple principles. 

NAS performance degradation. Gaming strategies often degrade the performance of other AOCs 

and non AOC flights, and most gaming combinations of the two AOCs degraded the overall 

NAS performance. Not surprisingly own performance can change radically depending on the 

strategy of the other AOC. 

Increase in system congestion. Gaming strategies are not concerned with managing demand 

within capacity constraints, and in general cause increases in airspace or airport congestion. In 

our experiments, all but the cooperative behavior aggravate system congestion. The observed 

congestion increases are not a major operational impact. Nonetheless, it is an impact of the 

gaming strategies and reveals that such gaming scenarios involve a third “player.” In addition to 

the two AOCs (or more generally N AOCs), there is also the service provider that may be 

impacted by the shifting of the demand profile and thus increase in congestion. That is, NASwide user gaming can be viewed as a third-party game: the participating airlines, nonparticipating airlines and the service provider. Of course such user-induced congestion is 

possible only to the extent that users can influence the strategic TFM initiatives. 

Unintended consequences. In addition to increasing delay of other AOC flights and non AOC 

flights, and aggravating system congestion, gaming strategies can ultimately have the unintended 

consequence of aggravating own fleet total delay. This consequence could serve as a gaming 

disincentive if TFM service provider strategies could be designed to ensure that such unintended 

delay occurs. 

Categories of gaming effects. We began this study with three categories that describe the user 

intent: cooperative, competitive and strategic. Our results suggest different types of outcomes 

that do not map to these intent categories. The effects are more complicated given there are 

various combinations of strategies implemented by the users. But we found that the results can 

be, at least in principle, categorized into four qualitative categories which are helpful in 

describing and grouping the impact of the gaming actions. First, a strategy (operating within a 

given environment) may improve own performance and also improve the overall NAS. We refer 

to such a strategy as egalitarian. Second, a strategy may improve own performance and 

degradation to other flights, if any, is less significant. Therefore the overall NAS performance 

improves. We refer to such a strategy as utilitarian. Third, a strategy may improve own 

performance and degradation to other flights is more significant. Therefore, the overall NAS 

performance degrades. We refer to such a strategy as aggressor. Finally, a strategy may degrade 

own performance but degradation to other flights is more significant. Obviously the overall NAS 

performance degrades. We refer to such a strategy as spoiler. 
42 

Best strategies. The best gaming performance, for own flights and for the overall NAS delay, we 

found was produced by the competitive-15 strategy. The strategic-surplus and cooperative were 

the next best, providing slight aggregate improvement. The strategic-WCRD provided enhanced 

or degraded results, depending on the other AOC-specific operating characteristics. 

Dynamic strategies. The effectiveness of AOC strategies depends on the strategies in use by the 

other users. This suggests that dynamic strategies could be designed to react to the strategies 

currently in use by the other AOCs. This could induce dynamic strategies to be implemented by 

other AOCs. 

Race the bottom. The possibility of gaming strategies reacting to other gaming strategies brings 

with it a range of possible system evolutionary pathways, including both equilibrium and 

divergent outcomes. For instance, one possibility is the race-to-the-bottom scenario, where 

gamers counter each other with spoiler strategies. The NAS devolves to further levels of 

decreasing performance as users successively activate such gaming strategies. 

Hybrid strategies. Sophisticated users would likely implement more complex hybrid strategies 

which simultaneously pursue different strategies for different markets, or even different flights, 

depending on the tactical scenario. 

4.2 Research recommendations 

In addition to the findings summarized in the previous section, this section summarizes questions 

raised by these findings, and the recommendations for further research. The recommendations 

are as follows 

Investigate causality. More research is required to determine the underlying causality of how 

gaming actions affect the performance of other AOC flights and overall NAS performance. How 

exactly is performance degraded or improved, and how can these causes be dis-incentivized and 

incentivized, respectively? 

Evaluate flight delay cost outcomes. In addition to the delay metric, future NextGen gaming 

research should evaluate the effect of gaming strategies on the flight delay cost results of the 

AOCs and non AOC flights. Is it possible, for instance, to degrade delay performance but 

improve (reduce) the overall flight delay cost? 

Refined investigations. We have developed various gaming strategies, but a further level of 

detail is needed to ensure these strategies are realistic. Also, additional strategies should be 

considered. 

Investigate the problem space. As we concluded above, the problem space is complex and large. 

Further investigation is required to explore the space and learn more about the impact of 

NextGen gaming strategies. 

Investigate incentivizing users. Further research is required to investigate service provider 

strategies and rules of the road to (i) create a gaming equilibrium so as to avoid undesirable 
43 

dynamics, chaos, and race to the bottom scenarios, and (ii) force users away from undesirable 

gaming strategies such as spoiler and aggressor strategies. 

Model dynamic strategies. Research is required to model and evaluate dynamic users who do not 

adhere to a static strategy, but use a time-varying strategy which switches between strategies 

depending on the actions of the other AOCs. 

Model hybrid strategies. Research is required to model and evaluate hybrid users who do not 

adhere to a global strategy, but use a dynamic-hybrid strategy which simultaneously implements 

different strategies in different markets or flights, depending on which is most profitable for each 

given scenario. 

Appendix A 

This section presents the delay multiplier values referenced in Section 2. 

Figure A.1 DM multiplier data. 
44 

References 

[1] Wambsganss M, “Collaborative Decision Making Through Dynamic Information 

Transfer,” Air Traffic Control Quarterly, 4:107-123, 1997. 

[2] Hunter G, Boisvert B and Ramamoorthy K, “Advanced Traffic Flow Management 

Experiments for National Airspace Performance Improvement,” Winter Simulation 

Conference, Washington, DC, December, 2007. 

[3] Hunter G., “Toward an Economic Model to Incentivize Voluntary Optimization of NAS 

Traffic Flow,” AIAA ATIO Conference, Anchorage, AK, September, 2008. 

[4] Performance Review Unit, Eurocontrol, "Evaluating the True Cost to Airlines of One 

Minute of Airborne or Ground Delay", University of Westminster Final Report, May, 2004. 

[5] Beatty R, Hsu R, “Preliminary Evaluation of Flight Delay Propagation through an airline 

schedule”, 2nd USA/EUROPE Air Traffic Management R&D Seminar, Orlando, 1998. 

[6] Waslander S, Raffard R, Tomlin C, “Market-based air traffic flow control with competing 

airlines,” submitted to AIAA Journal of Guidance, Dynamics and Control, February 2007. 

[7] Raffard R, Waslander S, Bayen A, Tomlin C, “A cooperative distributed approach to multiagent eulerian network control: Application to air traffic management,” in 2005 AIAA 

Conference on Guidance, Navigation and Control, San Francisco, CA, 2005
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Investigation of the Impact of User Gaming in the Next Generation 

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14. ABSTRACT

Over the past three decades, growth in the demand for air transportation has exceeded the growth in the national airspace system (NAS) capacity. Systems 

operating near capacity inevitably have delays and NAS delays have increased in recent years. The desire to minimize delay costs has placed attention on the 

NAS air traffic management (ATM) system.One initiative that has helped to provide user representation in the ATM solution is the collaborative decision 

making (CDM) process. CDM addresses this issue by bringing users (referred to here as airline operation centers [AOCs]) and ATM providers together for 

information exchange and cooperative planning. Such cooperative planning has been instituted, for instance, for the purpose of planning airport slot control 

strategies and rerouting strategies. While the CDM initiatives have met with much success, they have also introduced the potential for AOCs to manipulate the 

system in unforeseen, unintended, and perhaps undesirable ways, from a system-wide, synoptic perspective. This type of manipulation is sometimes referred to 

as “gaming” the system. This study uses a high-fidelity simulation tool to investigate several models of user decision making behavior which could be 

considered to be gaming behavior and the emergent system dynamics and interactions between AOCs and traffic management. 

15. SUBJECT TERMS

Air Transportation; Complex Decision Making; Gaming; National Airspace System

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