978-1-4577-0592-2/11/$26.00 ©2011 IEEE M6-1

TACTICAL AIRPORT CONFIGURATION MANAGEMENT Christopher A. Provan and Stephen C. Atkins

Mosaic ATM, Inc., Leesburg, VA

Abstract NASA has been studying a System Oriented

Runway Management concept that seeks to harmonize automation systems managing airborne and taxiing aircraft within a metroplex to achieve a more holistic traffic management solution. One element of the SORM initiative has been to develop a new automated decision support capability that can plan the airport configuration to optimize operational efficiency at an airport subject to traffic and weather uncertainty. While planning the runway configuration can provide benefit and is required by other automation concepts, larger benefits are possible by planning other airport configuration decisions, such as runway assignment policies, that are currently made manually by controllers. A laboratory prototype of the airport configuration planning algorithm, referred to as Tactical Runway Configuration Management (TRCM), has been implemented and studied within a simulation environment. This paper describes the TRCM algorithm as well as simulation results for three scenarios drawn from historical data at two airports under various weather and traffic conditions. The TRCM-recommended sequence of airport configurations are shown to result in significantly less delay than the airport configurations that were actually used by controllers, illustrating both the potential benefit from airport configuration optimization and the ability of the developed TRCM algorithm to provide effective airport configuration schedules. The application to several, distinct airports demonstrates the approach is capable of handling the differences between airports with a common algorithm, while providing decision support that respects the current procedures at those airports. Consequently, TRCM could be easily deployed to any airport within the National Airspace System. The algorithmic approach is also extensible to future operational scenarios, providing larger benefits in NextGen environments. NASA has begun the process of transferring the initial TRCM technology to the FAA.

Background One of the key objectives of the NextGen Air

Transportation System is a more efficient use of the available capacity within the National Airspace System (NAS). The 2007 Federal Aviation Administration (FAA) report Capacity Needs in the National Airspace System estimates that four of the nation's 35 busiest airports were operating at capacity in 2007 and that, even accounting for planned airport improvements, 10 more will reach their capacities by 2025 [1]. As the volume of air traffic across the NAS grows, airports and metroplexes – terminal airspaces shared by multiple high-traffic airports - are increasingly becoming bottlenecks to system-wide traffic flows. Efficient use of available capacities within these terminal areas will be vital to maintaining efficient operations across the NAS as a whole.

In response to this challenge, the National Aeronautics and Space Administration (NASA) has been studying a System Oriented Runway Management (SORM) concept that seeks to harmonize automation systems managing airborne and taxiing aircraft within a metroplex to achieve a more holistic traffic management solution on the airport surface and in the terminal airspace. One area of research within SORM has been to design and develop a prototype automated decision support tool for real-time tactical planning of terminal area resource-use policies: runway configuration, runway assignment policy, arrival/departure mix, taxi and airborne routing, and distribution of shared resources between airports within a single metroplex. These policies are collectively referred to as the airport configuration or metroplex configuration.

As detailed by Lohr and Williams, current practices in airport configuration management are most often localized and myopic [2]. Airports and metroplexes are typically restricted to a small number of configurations. Traffic managers choose between these configurations based mainly on current weather conditions or immediate congestion problems along with procedural constraints, such as noise abatement

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requirements. Configuration decisions are reconsidered only as required by these factors and are often made reactively to changing conditions without significant anticipation of future changes. Two primary recommendations by the authors for configuration management improvements are to better integrate weather forecasts into decision processes and to develop a decision aid that can monitor both current and predicted conditions and provide configuration management recommendations accordingly.

Under NASA’s SORM concept, we have developed the Tactical Configuration Management (TRCM) laboratory prototype automation tool to address these recommendations. TRCM is a real-time optimization tool that plans airport configurations to maximize the combined operational efficiency of the airport surface and terminal airspace within specified policy constraints. TRCM is able to provide configuration recommendations that account for weather forecasts and that respond to changes in traffic patterns, such as directional shifts (e.g., eastbound primary flow to westbound primary flow), changes in the arrival/departure mix, and increases or decreases in traffic density. Additionally, TRCM is able to account for uncertainty in both weather and demand forecasts.

Related Work The problem of managing air traffic in high-

density terminal areas has been addressed in the literature from a number of perspectives. Many authors have proposed improvements to arrival and departure scheduling and traffic management; see, for example, Atkin, et al, and Le, et al [3,4]. However, these approaches overlook the problem of airport configuration planning by either explicitly assuming that airport configurations are known and constant or by ignoring airport configuration completely.

A limited amount of existing research has addressed the problem of runway configuration planning. This research has largely been based on the discrete-time aggregate traffic model first introduced by Gilbo [5,6]. This model was revisited and enhanced by Bertsimas, et al [7]. Both the Gilbo and Bertsimas models are purely deterministic mixed-integer linear programming models that aggregate traffic into arrival and departure demand within discrete time intervals. Configurations are

modeled as two-dimensional capacity curves representing the tradeoff between the numbers of arrivals and departures that can be served at the airport in any single interval. The surface and terminal airspace are effectively ignored – flights queue at the airport according to the aggregate demand schedule and are served according to the configuration capacities.

The Bertsimas model was further refined by Provan and Atkins to incorporate weather uncertainty [8]. This new model was the basis for the Strategic Runway Configuration Management (SRCM) automation tool prototype, which has been studied under NASA’s SORM concept as a strategic capacity planning tool. Additional variations on this model have been studied by Duarte, et al, and Zhang and Kincaid [9,10].

The core TRCM model presented in this paper improves upon the above models in a number of ways. TRCM focuses on a shorter time frame than SRCM and the other variants of the Bertsimas model, allowing for a more comprehensive search model. Flights are modeled individually instead of in aggregate, and the surface and airspace themselves are accounted for in more detail. This allows for improved modeling of the effects of configuration decisions, such as travel times and fuel burn, that are not captured by a purely capacity-based model. The TRCM search model also incorporates uncertainty in both traffic schedules and weather forecasts.

TRCM Problem Description The objective of the TRCM automation tool is to

recommend an airport configuration schedule that allows for maximum efficiency of AMA and terminal area operations while complying with all relevant constraints on such a schedule. TRCM does not make control decisions for individual flights but instead operates at a tactical policy level. While there is no way to exactly predict controllers’ actions will operate within a given airport configuration, TRCM attempts to reasonably predict the aggregate effects of the chosen configuration schedule. Typical constraints on the configuration schedule include:

Airport configuration changes must be planned far enough in advance (e.g., 30 minutes) so that they can be coordinated between the TRACON and tower controllers.

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Major changes such as directional shifts in the runway configuration should not occur too frequently (e.g., no more than one change in any 30 minute interval).

Minor changes such as runway assignment policy changes should also be limited in frequency, but can occur more often than major changes (e.g., no more than one policy change every 15 minutes).

Noise abatement procedures and other local constraints must be followed.

In addition, the TRCM configuration schedule must account for the impact of weather. Weather can impact the configuration schedule in two primary ways. First, certain configurations may be unusable due to weather, such as when a runway is shut down due to excessive crosswinds or ceiling and visibility conditions below the thresholds of a runway’s instrument landing system (ILS) equipage. Second, the weather may impact the expected efficiency of a given runway configuration or operating policy. For example, arrival spacing may increase significantly when winds are strong enough to cause compression on the downwind leg for a given runway. Both types of weather affects are also subject to a large amount of forecast uncertainty.

Lastly, TRCM must incorporate uncertainty in traffic schedules. Traffic will need to be modeled out to a horizon on the order of two hours in order to properly anticipate the effect of configuration decisions. At this horizon, there is significant uncertainty in both arrival and departure schedules. TRCM must base its recommendations on a measure of expected efficiency that incorporates this uncertainty.

TRCM Algorithm TRCM computes its configuration

recommendations based on a simple enumerative search heuristic. The heuristic evaluates a cost function over a pre-defined search space of potential configuration schedules. The cost function is a flexible measure of efficiency that is computed based on the outputs of a fast-time simulation of airport operations under each candidate configuration schedule. The cost can include flight delays, travel time, predicted fuel burn, and penalty costs for flights operating in marginal weather conditions for their assigned route or runway.

The following two subsections provide greater detail on the search heuristic and the fast-time simulation airport model.

Search Algorithm The TRCM search heuristic is an enumerative

search algorithm. The algorithm begins with a list of flights F that are scheduled to enter the active movement area (AMA) or the terminal airspace between the current time and some pre-defined modeling horizon. We assume that all flights currently in the AMA or in the terminal airspace have been assigned runways and sequenced so that they will not be affected by any airport configuration changes that have not already been scheduled.

New configuration changes can be planned between a freeze horizon and a planning horizon

. We refer to the interval between the freeze and planning horizons as the planning interval. We define a generic information vector that includes all pertinent data outside of the flight list for a given scenario, including information on flights already operating in the terminal area, current airport configuration, any configuration changes planned before the freeze horizon, local constraints on configurations, and current weather conditions and forecasts.

It is assumed that there are K discrete airport configurations indexed from 1 to K. The constraints on the frequency of configuration changes will imply an upper bound M on the number of configuration changes that can occur during the planning interval. The decision variables are represented by matrices and . is a binary decision variable equal to 1 if the mth configuration change during the planning interval changes to configuration k and is equal to 0 otherwise. If then is the time that the mth configuration change is scheduled to occur. Otherwise, . for all k if fewer than m configuration changes are scheduled.

Given the above definitions, the TRCM search algorithm attempts to solve the following optimization problem:

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Cost function is described in greater detail

in the next subsection. The first set of constraints forces all new configuration change times to fall within the planning interval. The second set of constraints requires that only one new configuration be selected for each configuration change. The third set of constraints requires that a (m+1)th configuration change be scheduled only if a mth configuration change has been scheduled. The fourth set of constraints ensures that the configuration after each change differs from the configuration before the change. Constraint is a generic constraint that is assumed to include all constraints on the configuration change times that are implied by the information vector , including separations between consecutive configuration changes and local constraints. The final set of constraints ensures that the only take on the values 0 and 1.

Even without considering the characteristics of , the feasible space of configuration decisions

has non-linear boundary constraints due to the first set of constraints and discontinuous due to binary decision variables . Thus, regardless of the complexity of cost function , finding the globallyoptimal solution in this space would be computationally difficult.

The TRCM search heuristic instead searches for a minimizing solution over a smaller, discretized search space. We define a search interval and only allow for configuration changes to occur at multiples of t after the freeze horizon. That is, the only feasible values for the decision variables in are 0 and

for each such that .

Additionally, the heuristic places a smaller cap on the total number of configuration changes

allowed during the planning interval. The search space grows exponentially with the increase in the allowable number of configuration changes. Therefore, a small change in can result in a large reduction of the state space.

The TRCM search heuristic enumerates the candidate configuration schedules that fall within this reduced search space. Each candidate configuration schedule is then checked against the constraints of

for feasibility. Any feasible candidates are passed into the fast-time simulation airport model for evaluation. The configuration schedule with the minimum cost is selected as the recommended configuration schedule.

In our simulations, we have typically assumed a planning interval of 45 minutes and a search interval of 5 minutes. We also assume a maximum of 2 airport configuration changes in each planning interval. This implies a search space of approximately configuration schedules. However, many of these configuration schedules are rejected as infeasible due to the constraints of . In particular, constraints limiting the frequency of runway configuration changes to at least 30 minutes apart and policy changes to at least 15 minutes apart will eliminate the majority of the candidate schedules.

Airport Model The TRCM airport model is a fast-time

simulation model intended to quickly return a value for the expected cost function given flight list F, information vector , and configuration schedule .

The airport model is based on the simplifying assumptions that all conflicts between flights can be modeled as constraints on their relative runway times and that these constraints depend only on pairs of flights and information vector . For example, an airport configuration with a miles-in-trail constraint at the arrival fixes would be modeled based on the expected runway spacing between consecutive arrivals that are subject to that constraint. Expected compression effects or increased arrival spacing based on weather forecasts would be incorporated into those constraints. Similarly, flights departing from different runways whose flight paths cross will be assigned predicted runway times that are

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deconflicted based on the assumption that they will fly a constant speed to the crossing point.

Each airport configuration has an associated prioritization rule for scheduling flights at the runway. For example, an airport configuration might give prioritization to flights based strictly on undelayed wheels up or down times or might prioritize arrivals ahead of departures. Flights are assigned runways according to the policies in the active airport configuration. Their undelayed runway times are computed based on routing rules in the configuration, and flights are then sorted according to priority. Flights are deconflicted in priority order: each flight is assigned the earliest runway time after its undelayed runway time that does not conflict with any flights earlier in the priority list or any flights that are already active in the terminal area.

When a configuration change is scheduled, any flights with runway times prior to the change time have their runway times locked. A changeover delay may be applied that prevents all runway arrivals and departures after the configuration change for a specified length of time. All flights operating after the configuration change time are remodeled in the new configuration, and the process is repeated until all configuration changes have been modeled.

Based on the modeled runway assignments and times, projected gate arrival times, departure fix crossing times, distance, fuel burn, and delay metrics can be computed. These metrics can be compiled into a cost function. As the basis of the cost function for our prototype implementation, we define an effective delay metric intended to measure the combined effect of taxi routing, airborne routing, and congestion delays as predicted by the airport model. For a given flight , let terminal point be the flight’s departure fix if the flight is a departure and the flight’s handoff spot between the ground controller and the ramp controller (or the flight’s gate if no such handoff spot exists) for an arrival. For a given flight f, we define the terminal time

to be time that the airport model predicts f will reach under configuration schedule . We leave F, , and in the notation to emphasize that this time is an output of the airport model, which requires the complete flight list, the information vector, and the candidate configuration schedule as inputs. The optimal terminal time is defined as the earliest time that the flight could reach

across all possible configurations if it were not delayed by any other traffic. Then the effective delay of flight f is defined as:

.

Note that is constant with respect to the configuration schedule. This term can therefore be removed from the search objective function, implying that optimizing with respect to total effective delay is equivalent to optimizing with respect to total terminal time.

Weather uncertainty is modeled under each candidate configuration schedule using an estimated probability of the pilot of flight f rejecting the assigned runway based on the weather forecast for that flight’s runway time. This probability is multiplied by a “go around” penalty that roughly estimates the effect of a rejected runway assignment on overall flight delays. The concept of a user preference cost can also be incorporated into this penalty function. For example, a configuration schedule that results in flights being assigned to runways with significant forecast tailwinds or to shorter runways when the likelihood of precipitation is high might be penalized relative to configurations that make better use of airport resources with respect to weather.

Most weather products current available to air traffic controllers provide deterministic point forecasts of most weather factors – wind speed and direction, ceiling, visibility, relative humidity, etc. – along with a potentially probabilistic forecast of precipitation. Although it is beyond the scope of this work to produce a model that translates current forecast product outputs into the probabilistic estimate of aircraft behavior that our model requires, we believe that such a model is a plausible near-term solution for modeling the effect of weather uncertainty. For our proof-of-concept scenarios, we use a simple model that focuses on runway winds. If a flight is scheduled to use a runway with forecast tailwinds or crosswinds above specified thresholds then the flight is given a pilot balk probability of 1; otherwise, the probability is set to 0. The go around penalty for each flight with a balk probability of 1 is equivalent to a 15 minute delay.

Based on the results from the above airport model, we define the following cost function for flight list F and candidate configuration schedule :

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As noted above, the effective delay can

equivalently be replaced by terminal time for the purposes of the search algorithm.

The last element of the airport model is the inclusion of traffic uncertainty. The airport model as outlined above can be evaluated very quickly for a given list of flights with deterministic gate pushback times for departures or arrival fix crossing times for arrivals. We assume that the distributions and

describe the forecast errors for departure pushback times and arrival fix crossing times, respectively. For simplicity, we also assume independence in the errors between flights. We briefly discuss how we built the distributions used in our scenarios in the next section. It is not a requirement that the distributions be defined only for arrivals and departures; the distributions could be flight-specific based any relevant flight characteristics.

Given the error distributions, we create N independent flight lists by sampling N times from each flight’s corresponding distribution and applying the sample error to the flight’s predicted pushback or fix crossing time. The goal of the sampling is not to fully represent the sample space of flight lists, but to increase the stability of the TRCM configuration schedule recommendations. In our scenarios, we have found that there is a great deal of stability for sample sizes as small as 20.

Let be the ith sample flight list. Each is independently modeled under each candidate configuration schedule. The cost functions for each sample are averaged to produce an empirical estimate of expected cost for the candidate schedule:

This empirical expected cost is the objective function for the TRCM search heuristic. The candidate configuration schedule minimizing this cost is selected as the recommended configuration schedule.

Simulation Case Studies An initial prototype implementation of TRCM

has been developed in MATLAB. We present in this section the simulation results for three scenarios at two different airports illustrating some of the capabilities of TRCM. The first two scenarios are set at New York’s John F. Kennedy International Airport (JFK). The third scenario takes place at Orlando International Airport (MCO).

All three scenarios are based on actual traffic and weather data. Because our prototype implementation currently only considers weather in terms of tailwinds and crosswinds, we address only those elements of the weather forecast. In scenarios 2 and 3, the forecast winds fell below the threshold established for the pilot balk probability throughout the planning interval. Thus weather was not a factor in these two scenarios.

Our traffic prediction error distributions are based on an analysis of historical errors at JFK on a small number of days in 2009. We filtered the data by prediction lead time, defined as the difference between the time the prediction was made and the predicted pushback or fix crossing time. For the below scenarios, we only considered prediction lead times shorter than 60 minutes. We then attempted to fit a variety of distributions to this empirical data. A mixed normal distribution provided the strongest fit from the distributions tested for both arrivals and departures. The fitted probability density functions for the arrival and departure error distributions are plotted in Figure 1. These two distributions were used in all each of the three scenarios.

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Figure 1. Traffic Error Distributions

In both distributions, a positive error corresponds to a flight crossing the fix or pushing back later than predicted. Most arrivals predicted to enter the terminal airspace within an hour are already in the air. These flights are no longer subject to pushback or departure queuing uncertainty, and thus the overall uncertainty in the arrival fix crossing time prediction (Figure 1, top) is noticeably smaller than the uncertainty in the pushback time prediction (Figure 1, bottom). Both error distributions skew slightly positive. Each arrival has approximately a 5% probability of reaching its fix at least 10 minutes later than predicted. Each departure has approximately a 25% chance of pushing back at least 15 minutes after their predicted pushback time.

The TRCM search heuristic was run in each scenario with a sample size of 50 flight lists. We evaluated the TRCM recommendations in each scenario by generating a separate sample of 50 flight lists and comparing the outputs of the airport model

for those 50 sample flight lists between the TRCM recommended schedule and the actual historical schedule. Clearly we can expect TRCM to outperform the historical schedule since we are evaluating performance based on the same airport model over which TRCM is optimizing. Efforts are under way to build a more comprehensive simulation environment that will allow for more sophisticated validation and benefits analysis studies. However, we feel that this methodology is effective as an initial proof-of-concept of the TRCM prototype implementation.

Scenario 1: JFK Weather Shift Scenario The first scenario comes from the morning of

March 19, 2009 at JFK. JFK is part of the New York metroplex that includes LaGuardia International Airport (LGA), Newark International Airport (EWR), and Teterboro International Airport (TEB), along with many smaller airports. The airport diagram for JFK is shown in Figure 2.

Figure 2. Airport Diagram for JFK

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JFK has two sets of parallel runways: 4L/22R and 4R/22L; and 13L/31R and 13R/31L. There are four single-direction runway configurations using 4L/4R, 13L/13R, 22L/22R, and 31L/31R, respectively. Due to airspace constraints, each single-direction runway configuration allows for only a single-runway departure runway assignment policy. For configurations on 4L/4R, 22L/22R, and 31L/31R, there are two arrival runway assignment policies: single-runway and dual runway policies. In visual flight conditions, the parallel runways allow for independent arrivals. 13L/13R allows for only a single-runway arrival, single-runway departure policy because of airspace conflicts with LGA.

In addition, there are three frequently used multi-directional runway configurations. 13L,22L|13R arrives on 13L and 22L while departing on 13R; 22L|22R,31L arrives on 22L while departing on 22R and 31L; and 4R|4L,31L arrives on 4R while departing on 4L and 31L. The terminal airspace routing structure allows for the three runways to operate independently in each of these configurations with the exception of 13L,22L|13R. In this configuration, a flight cannot be cleared for departure on 13R while an arriving flight on 22L is between 1 and 3 nautical miles from the runway threshold.

The planning interval for this scenario is between 0745 and 0830 local time. The flight list for the scenario contained 64 flights: 40 departures and 24 arrivals. The arrivals were slightly more clustered early in the planning interval and the departures later.

The airport was operating in configuration 13L,22L|13R at the beginning of the planning interval with a slight tailwind on 22L. The forecast calls for increased wind speeds, with the forecast tailwind on 22L exceeding the specified threshold at 0800. Any flight scheduled to arrive on 22L after this time would be assigned a go around penalty in the TRCM objective function.

The actual configuration changed to 4R|4L,31L at 0800 to account for the wind shift as well as the increased departure demand. TRCM also reacts to the forecast wind shift; however, the recommended change occurs 15 minutes earlier at 0745 and changes to configuration 31R|31L, which assigns all arrivals to 31R and all departures to 31L. Clearly TRCM is able to choose a configuration that avoids assigning flights to runways 22L and 22R during the period that

the tailwind forecast exceeds the established threshold. Summary metrics comparing the predicted outcomes of the two configuration schedules are shown in Table 1. All metrics are the mean values across the 50 independent sample flight lists.

Table 1. Summary Metrics for Scenario 1

Metric TRCM Schedule

Actual Schedule

Total Effective Delay 267 min 294 min

Per Flight Travel Time 19.2 min 20.6 min

Per Flight Congestion Delay 104 sec 49 sec

The total effective delay is defined as in the airport model objective function. The per flight travel time is the average time that it takes a flight to travel from the departure gate to the departure fix or from the arrival fix to the arrival gate along its assigned route excluding any delays caused by traffic congestion. The per flight congestion delay is the average delay due to conflicts between flights.

The total effective delay across all 64 flights in this scenario is almost 30 minutes lower under the TRCM configuration schedule than under the actual configuration schedule. Because 31R|31L has only one departure runway compared to two for 4R|4L,31L, we would expect that the TRCM configuration schedule would create longer departure queues than the actual configuration schedule. Indeed, the per flight delay due to congestion is nearly 1 minute higher under the TRCM configuration schedule. Among departures only, the average congestion delay increases by 1 minute 25 seconds under TRCM. However, taxi distances are longer under the actual configuration schedule. For example, departures on 4L must taxi nearly ¾ nautical mile beyond the runway entry point for 31L to reach 4L. The shorter taxi distances result in an average travel time that is 1.4 minutes shorter under TRCM than under the actual configurations. This total reduction of nearly 1 hour of travel time across all 64 flights dominates the increased queuing times and leads to the overall reduction in effective delay.

Such a sophisticated analysis would be difficult for a human controller to perform in real time. Thus the potential value of TRCM in this scenario is not

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just in anticipating changes in weather but also in choosing an airport configuration schedule that balances the various operational implications that contribute to overall efficiency of operations.

Scenario 2: JFK Traffic Shift Scenario The second scenario also takes place at JFK.

Traffic data comes from historical data for May 6, 2009. Weather was not a factor on this day. The planning interval for the scenario lasts from 1720 to 1805 local time.

The input flight list contains 79 flights: 45 departures and 34 arrivals. There is a gradual shift from an arrival-heavy operation to a departure-heavy operation that occurs in the flight schedule between the start of the planning interval and approximately 1745. The airport was operating in a runway configuration using only runways 22L and 22R at the beginning of the planning horizon. The runway assignment policy assigned departures to 22R and split arrivals between 22L and 22R.

The actual configuration changed to 22L|22R,31L at 1730 in anticipation of the shift in traffic. TRCM recommends two airport configuration changes, first to 13L,22L|13R at 1720 and then to 22L|22R,31L at 1800. Note that TRCM delays the switch to a departure-heavy configuration until 1800 even though the traffic shift is scheduled to occur by 1745. Recall that the error distribution for forecast pushback times skews to the positive, meaning that departures will push from their gates late more often than they will push early. TRCM anticipates this delay in the start of the departure push and calls for a later transition to a departure configuration in order to allow higher arrival rates for as long as possible.

The summary metrics for Scenario 2 are shown in Table 2. The overall reduction in effective delay is 22 minutes over the 79 flights. In contrast to the previous scenario, the TRCM configuration schedule results in slightly higher average travel times. However, due to the delayed switch to a departure configuration, the average congestion delay is reduced by just over 30 seconds.

Table 2. Summary Metrics for Scenario 2

Metric TRCM Schedule

Actual Schedule

Total Effective Delay 395 min 417 min

Per Flight Travel Time 19.9 min 19.5 min

Per Flight Congestion Delay 104 sec 140 sec

Scenario 3: MCO Departure Policy Scenario The last scenario is set at MCO on the morning

on October 13, 2010. The airport diagram for MCO is shown in Figure 3.

Figure 3. Airport Diagram for MCO

MCO has four north-south runways: 18R/36L, 18L/36R, 17R/35L, and 17L/35R. There are two primary runway configurations: north flow and south flow. For both directions of flow, the primary configuration uses the two inboard runways (18L/36R and 17R/35L) for departures and the two outboard runways for arrivals. Arrival runways are

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independent from each other and from the departure runways. Departure runways are dependent only when the departure fixes of a pair of flights requires that their flight paths merge or cross. When this occurs, the runway times of the two departures must be separated by at least two minutes. Since the majority of traffic is arriving from or departing to other domestic airports, runway configuration policy is fairly straight-forward under calm weather conditions: north flow during departure pushes and south flow during arrival pushes.

Because the runway configuration policy is trivial, we instead focus this scenario on the departure runway assignment policy. In both runway configurations, departure runway assignments follow one of two policies: taxi for convenience or taxi for direction. Under taxi for convenience, flights are assigned to the departure runway closest to their gate. Unimpeded gate to fix travel times are lowest under taxi for convenience. Under taxi for direction, departing flights are assigned to the runway nearest to their departure fix. This prevents any crossing or merging of departure flight paths, which allows the departure runways to operate independently of one another.

The planning interval for Scenario 3 is from 0730 to 0815 local time. The traffic is primarily departures during this time period. The actual configuration used was a north flow under taxi for direction. TRCM also chooses a north flow during this departure push but recommends an immediate departure runway assignment policy change to taxi for convenience at 0730. Because arrivals are independent from departures, there is no change to the modeled arrivals between the actual and the TRCM configuration schedules. Thus the below summary metrics are computed for the 38 departures on the flight list only (see Table 3).

Table 3. Summary Metrics for Scenario 3

Metric TRCM Schedule

Actual Schedule

Total Effective Delay 9.5 min 42.1 min

Per Flight Travel Time 21.1 min 22.0 min

Per Flight Congestion Delay 15 sec 13 sec

The TRCM recommended configuration schedule results in a total effective delay across departures of only 9.5 minutes. Since taxi for convenience minimizes undelayed travel times, this effective delay is entirely the result of the 15 second average per flight queuing delay. Under the actual configuration of taxi for direction, the average taxi distance for departures increased by approximately ¼ mile, resulting in an increase of almost 1 minute per flight in undelayed travel time. The benefit of deconflicting the departure runways was an expected reduction in queuing delays of only 2 seconds per flight, or less than 1 minute total across all departures. Thus the total effective delay jumps to 42.1 minutes under the actual airport configuration.

In addition to demonstrating the TRCM prototype implementation at a second airport, this scenario also suggests the potential of using TRCM as a training and research tool. While it may appear to a human controller that taxi for direction improves efficiency because of the reduced departure queues and deconflicted departure runways, TRCM clearly demonstrates the negative effect of this policy on the efficiency of departure operations in this scenario. TRCM might similarly allow users to analyze the benefit of introducing new runways, configurations, or policies or to explore future operational scenarios as part of NextGen.

Conclusion and Future Research This paper describes the Tactical Runway

Configuration Management (TRCM) tool that has been developed under NASA’s System Oriented Runway Management (SORM) concept. TRCM is an automated decision support tool intended to improve airport configuration scheduling under both traffic and weather uncertainty. TRCM uses a simple search heuristic and fast-time simulation airport model to choose an airport configuration schedule, including runway configurations and runway assignment policies that allow for maximum combined efficiency of AMA and terminal area operations.

Using a laboratory prototype of the TRCM software, we have demonstrated in this paper the potential benefits of TRCM in a simulation of three real-world scenarios taking place at two distinct airports. TRCM was able to choose configurations that responded to shifts in weather conditions and

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traffic characteristics and that balanced the impacts of travel times and airport congestion. These scenarios also demonstrated the potential of using TRCM as a training and research tool.

NASA is in the process of transferring the initial TRCM technology to the FAA with the goal of building a prototype field version of TRCM. NASA is also continuing to pursue research on TRCM under the SORM concept. Current efforts are focused on integrating TRCM with flight-level decision support tools being developed under SORM and on developing the Metroplex Simulation Environment (MSE), a surface and terminal airspace simulation platform that will be used for more comprehensive benefits analysis of TRCM. Future research will extend the TRCM concept and algorithm to coordinating configuration planning across airports in a metroplex and will study TRCM as a tool for evaluating the impact of future NextGen concepts on configuration planning and terminal area operations.

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[3] Atkin, Jason A.D., Edmund K. Burke, John S. Greenwood, Dale Reeson, February 2007, Hybrid Metaheuristics to Aid Runway Scheduling at London Heathrow Airport, Transportation Science, Volume 41, Number 1, pp. 90-106.

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[9] Duarte, Michael, Christopher Weld, Rex Kincaid, April 2010, A Runway Configuration Management (RCM) Model with Marginally Decreasing Transition Capacities, Systems and Information Engineering Design Symposium, University of Virginia.

[10] Zhang, Rui, Rex Kincaid, 2011, Robust Model for Runway Configuration Management, working paper.

Acknowledgements The work described in this paper was supported

by the National Aeronautics and Space Administration, Airspace Systems Program through contract NNL09AA02B. The authors would like to acknowledge in particular the contributions of Mr. Gary Lohr and Mr. Paul Stough at NASA Langley Research Center.

2011 Integrated Communications Navigation

and Surveillance (ICNS) Conference May 10-12, 2011