American Institute of Aeronautics and Astronautics
Improved Prediction of Flight Delays Using the LMINET2 System-Wide Simulation Model
Dou Long, Ph.D., Shahab Hasan1 LMI, McLean, Virginia 22102-7805
Abstract
In this paper we present LMINET2, a national airspace system (NAS) simulation model used to estimate flight delays and cancellations in all operating conditions including off-nominal conditions such as inclement weather. We outline its design, calibration, validation, and finally, its application to predicting the NAS-wide flight delays in the future. Beyond the estimation of queuing delays, it also explicitly models the impact of flight delays on downstream operations, such as delayed departure, flight cancellation, and Ground Delay Program (GDP).
I. Introduction
The air transportation system provides a vital economic infrastructure in the United States and in the world for the flow of passengers and goods. Although the recent economic slowdown has dampened the traffic demand, anticipated long term economic and demographic growth will resume demand growth over time. Unless the NAS capacities keep pace with the forecasted demand, air traffic congestion will become a critical problem. To accommodate the traffic growth, the Joint Planning and Development Office (JPDO) is researching and developing the Next Generation Air Transportation System (NextGen).
Because of its flexibility and fast set up and execution, LMINET has been used to estimate the benefits of various air traffic management (ATM) technologies sponsored by NASA or FAA. In particular, LMINET has been the primary analysis tool to estimate the throughput benefit of NextGen programs [Long et al., 2003]. While the throughput is mostly determined by the planned flight schedules which are mostly determined by operations/delays in good weather, a common practice for U.S. carriers, we realize that LMINET is not particularly suitable to estimate the flight delays in bad weather conditions because of its lack of flight delay propagation and cancellation. While the lack of these modeling components does not pose a serious problem for the throughput estimates, because they are primarily determined by the good-weather flight schedules, it does represent a gap for modeling the NAS in bad weather; this gap is important because many NextGen concepts are intended to improve NAS performance in such conditions. While we maintain that the traffic throughput is one of the most important capacity benefit metric of NextGen, the flight delay is equally important. These two metrics are complementary to each other because while the throughput is mostly determined by schedules which are mostly based on the good-weather operation, most of the flight delays in a given year are caused by a small number of bad weather days [Long et al., 2005]. Therefore we need to have a NAS operations model that can be used to predict the flight delays and cancellations in all conditions including the off-nominal operations. LMINET2 is designed just for this purpose.
The NAS congestion and hence the flight queuing delays are the fundamental cause of flight delays in the NAS. In addition to the flight queuing delays, a NAS-wide operation model must also consider the ramifications of queuing delays, including delay propagation and flight cancellation. To capture these effects, we need first to have a model to generate flight schedule delays from their queuing delays, second to have an aircraft itinerary model to connect flights at airports, and finally an aircraft turnaround model to represent the ground operations. Because of the nature of network operations and the fact that congestion results from all flights, we also need to comprehensively deal with all sorts of flights, including general aviation (GA), flights to the smaller airports where the airport capacities may not be a problem, and international flights. Other phenomena of NAS disruptions, like flight cancellation and GDP must also be part of the model. Explicitly, before we actually started building LMINET2, we developed the following requirements:
1 Senior Member, AIAA.
9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO) <br>and <br>Air21 - 23 September 2009, Hilton Head, South Carolina
It should cover all flights in the NAS, commercial and GA, domestic and international, operated under Instrument Flight Rules (IFR) and Visual Flight Rules (VFR).
It should estimate flight queuing delays at major airports as part of its output.
It should estimate flight schedule delays as part of its output.
It should capture the impact of flight schedule delays, including delay propagation and flight cancellation.
It should capture proactive air traffic management (ATM) procedures such as GDP that stem from NAS congestion.
II. Overview of LMINET2
Our conceptual overview of a NAS-wide operations simulation is depicted in Figure 1 and consists of three major models: airport, airspace, and airline operations control (AOC). The airport model generates the flight delays in the airport operations including the vicinity airspace, runway, taxiway, and gate, given the flight schedules and weather conditions. The airspace model generates the flight en route delays given the flight plans. The AOC model mimics the functions of an airline operational control center and the air traffic service provider, which make schedule adjustments including flight delay propagation and cancellation. The Preemptive Rescheduler models the functionality of the airlines to change the flight schedule given a long term forecast, which will reduce flight delay and help the flight connection. The Flight Re-router models the flexibility of the airlines to change the flight route to avoid weather cells or airspace congestion. To deal with the disruption of flight operations, the Flight Re-connection Module and Dynamic Rescheduler model the dynamic changes to the flight connections and schedules to reduce the further cascading of flight delays.
Figure 1. General NAS Operation Simulation Schematic
Flight throughput and delay
Load flight schedule and trajectoriesInitialize simulation time
Note that the scheme depicted in Figure 1 is intended in a general sense and the design of any particular model may have different flavors. For LMINET2, its AOC module does not include the airspace model, the functionality of
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flight rerouting by the AOC. This design decision was made considering that the en route delay is a small portion of total flight delay, even in bad weather [Long et al., 2005]. It does not make a significant difference to simulation results in terms of flight delays and cancellations to lack an airspace model in a NAS-wide simulation unless one is particularly interested in en route airspace operations. Still, we do intend to add this component in a later version of the model. In terms of functionality and data flows, the schematic of LMINET2 is shown in Figure 2.
Figure 2. Schematic of LMINET2
Flight Schedule Delay &
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The inputs to the Airport Queuing Delay Model are the arrival and departure demand schedules, which are the total number of arrivals and departures, respectively, in every 15-minute window throughout a day, and the outputs are the arrival and departure queuing delays from the runway and taxiway congestion. Once the arrival queuing delays are generated, the Flight Schedule Delay and Cancellation Model will first translate the queuing delays to schedule delays which include consideration of departure gate delay and schedule pad. Then the arrival schedule delay is fed to the Aircraft Connection and Turnaround Model to adjust the departure delay and time of the next leg of the aircraft, which will further change the airport departure demand. The three models operate on three different entities: demand, flight, and aircraft, respectively. Demand is the total number of arrivals or departures,. A flight is specified in the schedule in terms of origin, destination, times of departure and arrival, and equipment. An aircraft is a physical asset, designated by its identification number (i.e., tail number).
III. Model Components and Calibration
Flight Connection Model. The future flight schedule typically is in the form as it appears to the public including the origin and destination of each flight with departure and arrival times and equipment type, generated by the demand forecast model. But this is not enough for the NAS-wide simulation study. The model sets up the connectivity based on the flight schedule for all the flights in LMINET2, which is used for delay propagation and flight cancellation. For each flight with a given scheduled arrival time, the model connects it with a flight of the same seat category in a specified departure time window. While the flights can connect only to the same equipment type of the same carrier in real schedule, we ignore the carrier flag in our model. The reason is that, regardless of how the schedule is given, we do not believe it is reliably possible to know the industry configuration and the aircraft type of the same seat category in the future. Otherwise, using the departure time window is the standard way that the airlines connect flights. So when we estimate the windows based on the current flight schedules, given by OAG in 2006, we also ignored the carrier and equipment type and used seat category only. This degrades the accuracy of the model when running with the current traffic as input, but it provides a more realistic setting when running with future traffic, which is the purpose of the model. The windows are set at the lower and upper 5th percentiles of the time lapse of departure based on 2006 OAG schedules according to the flight seat categories. The seat categories used in LMINET2 are 0, 15, 35, 50, 70, 100, 130, 180, 250, 350, 500, and 800 seats. We note that the model is run first in the overall simulation sequence and only for the commercial flights, and not all commercial flights are necessarily connected. We note that the model can also be run dynamically to support the preemptive and dynamic scheduling in LMINET2 to revise the flight itineraries given the revised flight schedule. But this feature is not turned on for predicting future flight delays in the case study to be presented in the paper in order to be conservative in assuming NextGen functionalities.
Airport Queuing Delay Model. For this component we use the previous version of LMINET which models the flight queuing delays at airports based on queuing theory. At each airport, arrivals enter the arrival queue, qA, according to a Poisson arrival process with parameter A(t). An arrival flight first receives service from the arrival runway server, it then enters the taxi-in queue, qta. After exiting the taxi-in queue, the aircraft experiences a
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turnaround delay, , and enters the ready-to-depart reservoir, R. Departures, based on departure demand, enter the queue for aircraft, qp, according to a Poisson process with rate D. Having secured a ready-to-depart aircraft, the departure leaves qp and enters the queue for taxi-out service, qtd. Output from the taxi-out queue is input to the queue for service at a departure runway, qD, where it is served according to the departure service process with rate D. The process is shown in Figure 3.
Figure 3. Schematic of LMINET Airport Delay Model
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The delays are based on the solutions of dynamic queuing equations. The intensities of the Poisson processes are assumed to be constant within every 15-minute time interval, which are determined by the number of flights therein.
Airports and Capacities. The queuing delay calculations are performed at 310 airports, of which 110 airports’ capacities are based on the ones used by the JPDO and FAA, while the rest have been developed by LMI from the airport layouts and their facilities. The 310 airports selected for modeling represent 98.6 percent of all air carrier activity as measured by operations, 99.8 percent of air carrier enplanements, and 72 percent of all air taxi operations. We assume there are no queuing delays in other airports, but the model does account for the fact that a commercial flight may still carry schedule delays at those airports which expands the number of airports with possible schedule delays to 781. The taxiway capacities are fitted, by running LMINET, to the reported taxiway delays as reported in 2005 ASPM. Figure 4 shows the location of the 310 airports in CONUS.
Figure 4. Locations of the 310 Airports in LMINET2
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Flight Gate Arrival Delay Model. Here we are concerned about the flight schedule delay at the arrival gate. The schedule delay at the arrival gate would seem to simply be the sum of all delays of the flight: at the departure gate, taxi out, en route, taxi in. But, in reality, that is not the case as shown in Figure 5.
Figure 5. Flight Delays by Stages
Taxi In
(1.71)
Taxi Out
(4.68)
Departure Gate
(10.98)
Arrival Gate
(7.55)
En Route
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Taxi In
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Taxi Out
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Departure Gate
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Arrival Gate
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The numbers in parentheses in Figure 5 are the average delay minutes for all the flights as recorded in ASPM in 2005. The sum of the delays up to the gate arrival, from departure gate, taxi out, en route, to taxi in, is 18.64 minutes, which is 7.55 minutes more than the recorded arrival gate delay. Their difference, 11.09 minutes, is the schedule pad that the airlines have put in the schedule in order to meet the on-time target. Obviously, 11.09 minute is the average schedule pad across all flights in NAS, and the schedule pads are different depending on the market and the airlines. In LMINET2, schedule pad can be an input for each individual flight, but we have used the 11.09 minute as the default for all but a few individually adjusted markets when calibrating the model.
Ground Turnaround Model. This model is for estimating the delay propagation. The model is constructed from the empirical data and the premise that there is a minimal time that the aircraft has to stay on the ground before being ready for the next flight, allowing for passenger deplanement and enplanement, refueling, mechanical check-up, etc. If the aircraft gate arrival time plus the minimal ground turnaround time is before the departure time of the flight of the next leg, then there is no departure time delay for the next leg. Otherwise, the departure of the next leg will be delayed the extra time beyond the scheduled departure time. The minimal ground turnaround times, a function of aircraft seat category, are set at the lower 5th percentile according to the OAG schedule in 2005.
The empirical data also suggest that there is another kind of departure delay other than the propagated delay from the previous leg. It is shown in Figure 6.
Figure 6. Flight Ground Delays
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Each point in Figure 6 is given by the predicted ground delay according to the formula above and the observed delay. It is for all flights as recorded by Airline Service Quality Performance (ASQP) in June 2005. We can see that the model works very well in general, where the red line is the reference line that the two delays are equal. However, we do see that there seems to be an intercept that there is a delay even when the predicted departure gate delay is zero. We believe there is a reason for this because the model above has just captured the delay propagations only, it has not considered other causes of departure delay such as waiting for connecting passengers, delays caused by mechanical check up and by crew readiness, etc. Modeling of such factors is beyond the scope of our model, but they can be represented by an adjustment factor in the ground turnaround model. So we introduce a correction factor that is added to the above formula to capture all causes other than the delay of the previous flight. By statistical analysis, based on all flights as recorded in ASQP of June 2005, it is given by 7.6 minutes, and R2 = 0.94.
Ground Delay Program. GDP is an ATM program that holds flights at departure airports when the predicted arrival rate at the destination airport exceeds its capacity or the airspace to be traversed is overly congested. Without GDP the flight would have to hold above the arrival airport or en route airspace waiting to be accepted. GDP moves the otherwise expected airborne delay to the ground, thus (1) reducing the air carrier cost of delay, (2) allowing more orderly ATC operation, and (3) reducing the ATC sector congestion. In the current ATM, if the lead time of the predicted bad weather is long enough before the departure time of a flight going to the congested airports, it can be rescheduled to a later time. Since LMINET2 does not currently include an airspace delay model, we only need to model the GDP issued due to congestion at the airports. The implementation of the ground delay module is through the feature that LMINET2 keeps two time clocks: one is to simulate the operations of the NAS, and the other is for GDP detection and implementation which runs two hours ahead of the operations module. The selection of two hours is based on our reading of GDP programs issued in the past, which have varied around 2 hours. In the GDP module, once the arrival demand exceeds its capacity, then some flights will have to be delayed. In the implementation, the arrival capacity can take the form of either the published AAR or the capacity returned from the airport runway capacity Pareto frontier. Because we envision that LMINET2 will be used only with the input of a feasible flight schedule, which makes the simulation meaningful, the GDP can be initiated only when there is bad weather although it can last beyond the period of the bad weather. We use an iterative procedure to delay the departure times of flights: for the first time epoch of 15 minutes, if the demand exceeds the capacity, then the algorithm first searches for all the flights that have not yet departed, and then randomly selects the flights and delays them by 15 minutes. In the next 15 minute time epoch, those delayed flights have the priority to depart if they can fit within the capacity. It is a first-in, first-out scheme that runs until there is no schedule-delayed flight.
Flight Cancellation Logic. When the NAS capacity is constrained, flights can be cancelled for a variety of reasons. Although the cancellation percentage is very small and not enough to change the system throughput significantly, cancellations are, nonetheless, included in the implementation of LMINET2 not just for the cancellations themselves but also, perhaps more importantly, for the flight delay estimation in a more realistic setting [Long et al., 2005]. Flight cancellation is a standard practice by the airlines to sacrifice some flight service in order to maintain some degree of schedule integrity of other flights under off-nominal conditions, because a small imbalance of demand over capacity can cause horrendous queuing delays and hence the schedule delays. This is a trade-off by the airlines since flight cancellation involves some kind of financial compensation to the passengers and the long flight delays can cause the good-will of the passengers from the future booking. Through survey, we have found that the major U.S. air carriers have different objectives, rules, and sophistication when cancelling flights under off-nominal condition. Some favor the speedier schedule recovery while some favor the minimization of financial loss, i.e., flight cancellation with a longer period of schedule recovery. We do not believe it is a good idea just to implement one particular set flight cancellation rules of one carrier, especially we do not even know the industry composition in the future. Rather, we believe it is a good idea to establish the rules from the empirical data, ignoring the flight carrier flag to be consistent with our design objective of LMINET2 to predict NAS operations in the future. In summary, flights in LMINET2 can be cancelled for the following reasons.
1. If either the departure or arrival queuing delays in one time epoch, due to the congestion at the runway and taxiway, exceed the input delay parameters, then all the GA operations are cancelled. If the queuing delay still exceeds the input delay tolerance parameter, the commercial operations will be randomly selected to be cancelled until the input delay tolerance is met. When either departure or arrival operation is cancelled, the entire flight operation is cancelled.
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2. For a commercial flight, if its scheduled departure time is delayed more than an input parameter, then the flight is cancelled.
3. In the GDP program, if the revised schedule is delayed more than an input tolerance parameter, then the flight is cancelled.
4. Any flight can be cancelled randomly to reflect the flight cancellation due to other reasons such as mechanical failure, etc. This probability is very small, only a fraction of one percent of the total ASPM flights in 2006 [Long et al., 2005].
5. If a commercial flight is cancelled due to any of the reasons above, then its next leg is also cancelled. We do not cancel all the flights that are linked through the itinerary established at the beginning of the simulation because we want to preserve the flexibility of the airlines to use dynamic scheduling and the use of spare aircraft, which are commonly employed by the airlines. Cancelling the next leg works especially well for the shuttle flights between two city pairs in that the cancelled flight deprives the return flight of the needed aircraft. This logic, however, does not prevent a flight cancellation during a later time of the day. For example, the third leg of the cancelled flight is not immune from cancellation; it can be cancelled for its own reason.
Model Parameter Selection and Calibration. We do not believe that the parameters selected by us are the best per se for each individual model. There are two factors we need to keep in mind when selecting the model parameters. First, one has to select the parameters that best reflect the input data. For example, if we use the airline flag to connect the flights, then we may get a better flight connection and henceforth a better delay propagation and flight cancellation matching. But we have to ignore the carrier flag in LMINET2 because we do not think it is a piece of reliable information we would have for a future flight schedule. Second, the model is integrated such that any change to one parameter will change the simulation results and parameter calibration everywhere. In the above example, the calibration of the connection parameter will change the calibration in other parts of the model. The focus of the modeling should not be on the test of individual models, but rather on the key statistics important to the decision maker. This is the topic of the next section.
IV. Model Validation
The validation of LMINET2 was carried out by comparing the model output with the traffic statistics recorded in ASPM. The following statistics in ASPM were compared directly with the results from LMINET2:
Flight arrival gate delay. Along with throughput, this is the most important statistic in quantifying the operational and economic benefits of NextGen.
Flight arrival on-time percentage. According to the Department of Transportation (DOT), a flight is considered to be late if its arrival gate delay is more than 15 minutes, otherwise it is considered to be on-time. It is a derived statistic from the arrival gate delay of each flight, but it is useful to communicate to the flying public about the punctuality of flight service.
Arrival/departure cancellation. In validation, we will focus on the bad weather days with a large number of cancellations. The cancellations on good weather days are very few and random.
Departure gate delay. The airlines use this measure to manage punctuality of their schedule, but it is not very important in benefit studies. We used it to test the inner working of the model.
Taxi out delay. This is defined by DOT as the extra time an aircraft taxies beyond the nominal time. The delay can be caused by many reasons from the runway and taxiway congestion to the airspace GDP and congestion. We think it is nonetheless close to the departure runway and taxi out queuing delays in our model, especially in good weather.
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There are other statistics that ASPM collects that cannot be matched to any LMINET2 outputs. Since LMINET2 currently does not have an airspace model, we are unable to generate any airborne delay. Taxi in delay is another one we cannot use because of conflicting definitions in our model versus the data source. In ASPM, taxi-in delay is defined as the extra time beyond the nominal time, while LMINET2 defines the taxi-in delay as the delay caused by the congestion. Other than these two, LMINET2 can generate all the statistics in ASPM.
We selected three days to compare the statistics: February 9, 2004, which is a relatively good day; May 10, 2004, which has some bad weather in Chicago area; and July 27, 2004, which has bad weather in the East Coast. In running LMINET2, we used the published OAG schedules as the schedules for the commercial flights, and the generated schedules for the GA flights [Long et al., 2001]. The comparison of the statistics is carried out at the 53 airports common to both 75-ASPM airports and 56-FACT2 airports where the airport capacity models are more reliable.
Figure 7. Weighted Averages of Errors
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Figure 8. Weighted Averages of Absolute Errors
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Figure 7 shows the averages of errors, weighted by the operations at the airports, for the three test days. The vertical axis is not labeled as there are three different measures being reported: the bars are in terms of minutes for delay, numbers for the cancellations, and percentages for the arrival gate ontime statistics. From this chart, one can see that predicted arrival gate delays are a few minutes off the recorded statistics on the average, and the average cancellations are off by a few for all airports. Since they are close to 0, this means LMINET2 is not biased and can represent the system average statistics fairly well Figure 8 shows the absolute percentage errors for delays, cancellations, and the ontime arrival percentages. Here the errors are amplified in that there is no such cancellation effect of positive and negative errors, and also the percentage errors can be large even if the absolute errors are small. Overall, we believe the errors at the system level are good enough to accept.
Beyond the averages across the 53 airports, we think it is also very important to compare the traffic statistics at individual airports in various conditions, especially in bad weather conditions. We have done that for all three days, and for all the statistics that can be compared. The following are just examples of the validation we have done.
Figure 9. Arrival Gate Delay in 5/10/2004, by Airport
Figure 10. Arrival Cancellation in 5/10/2004, by Airport
In Figures 9 and 10, the red and blue bars represent the recorded and model simulated statistics, respectively. They show that the model results compare to the ASPM statistics fairly well. In particular, the model tracks the
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delays and cancellations at Chicago (ORD) very well. Similar validation for the other two days were also carried out, which are omitted here, and also show close matches between the recorded and model simulated statistics.
Overall, we think the model does very well in duplicating the NAS performance results in various conditions and in the key performance metrics. One should be reminded that some errors are inevitable due to many causes. First, our traffic schedules are not the real ones in the sense that our commercial schedules are those published without consideration of charter flights and last-minute cancellations or schedule changes, and our GA schedules are simply the ones generated by another demand generation model. Second, the granularity of our model prohibits tighter precision of the model, as we discussed before, which we think is actually a good feature we want to preserve for its primary use in simulating future scenarios. But through careful analysis, we believe that most of the errors are caused by the input data. Among them the airport capacities are a major culprit. In LMINET2, the capacities need to be the theoretical upper bound of the airport operation, while in many cases the capacities we have used as input are based on the actual field observations or the operational guidelines. Another source of error related to capacities is that there is only one capacity Pareto curve in the IMC operations. Depending on the weather, the airport configuration in IMC can be quite varied and henceforth would have different Pareto curves. Weather is also a major error contributor in the sense that in the validation we only have VMC/IMC designations. With more information on the weather such as wind speed and direction, the airport capacity can be better allocated including marginal VMC. But the airport capacity and weather data are inputs to LMINET2 and are beyond the scope of our modeling. Our validation, nonetheless, has indicated to us the likely error bound when simulating a NextGen scenario in the future and the places that we can improve the simulation accuracy.
V. Application to NextGen Analysis
Both LMINET and LMINET2 have been employed by the Interagency Portfolio and Systems Analysis Division of JPDO to quantify the benefits of proposed NextGen technologies. Through a case study of one proposed NextGen in 2025 while focusing on the delay estimates, we show the process and preliminary results. In this case study, we have kept all the model parameters, such as the schedule pads, ground turnaround times, etc., unchanged as calibrated by the three days in 2005. Therefore, the running of LMINET2 is essentially composed of preparing its three input files: flight schedule, airport capacities, and airport weather.
Flight Schedule. We used the projected flight schedule in 2025 under the NextGen capacities, which is the trimmed flight schedule. The original unconstrained flight schedule is generated according to the FAA Terminal Area Forecast (TAF) issued in 2007, which also assumes that there is no change to the airlines operations including the time patterns. In other words, the unconstrained flight schedule forecast is the default forecast, without the change of airline operations such as hub-and-spoke, and without the explicit consideration of whether the NAS has the capacity to sustain such operations. Although LMINET2 is capable of simulating the NAS and estimating the flight delays at any traffic level, the results are meaningful only with realistic flight schedule. We trimmed some flights from the unconstrained flight schedule via running LMINET so that there is no traffic demand exceeding NAS capacity everywhere in the NAS. For the NAS capacities, they are specified at 310 airports and for every air traffic control (ATC) sector, for every 15 minute interval. At each of the 310 airports, the demand in each 15 minute epoch cannot exceed 120% of its capacity, and the demand in each rolling hour cannot exceed 90% of its capacity. For each ATC sector, its demand for every 15 minute epoch cannot exceed the Monitor Alert Parameter (MAP) of the sector. Flight trimming in this stage is fundamentally different from flight cancellation, where trimming refers to the identification of flights that would have appeared in the schedule if the NAS were to have no capacity constraints while the cancelled flights are the ones removed from the flight schedule due to NAS disruption. Since the schedule trimming assumes universally good weather in the NAS, the additional flight delays and cancellations under LMINET2 runs are caused by the capacity reduction due to the weather conditions. As a result of trimming, 3,279 out of the total of 79,166 IFR flights in the unconstrained schedule are trimmed in a hypothetical day in 2025, which is about 4.1% and are concentrated at a few airports. In addition, 553 GA flights are also trimmed.
NAS Capacities. For the top 110 large airports, their baseline capacities for 2007 are first constructed from FACT2 and their capacities in 2025 are based on their baseline capacities, planned runway construction, and NextGen improvements. There is a wide range of capacity increases at these airports from around 20% to over 70% from their baseline capacities in 2007, and the average good weather capacity improvement in the OEP 35 airports is 42%. For the bottom 200 airports in the 310 airport list, we first constructed their baseline capacities in 2007, and
their capacities in 2025 are assumed unchanged from their baseline except for a few with planned runway construction. No NextGen technologies are assumed at these 200 airports in 2025. We assume there are no ATC sector changes in 2025 except that they have larger MAPs. The percentage increases for most of sectors are about 70%, and the rest are individually adjusted with even higher percentage gains. LMINET has identified only a handful of sector capacity violations in 2025.
Weather Files. 7 days are selected from the real weather in 2007, which are shown in the following figures. The airport capacity curves are selected based on the ceiling and visibility at each airport.
Figure 11. Comparison of Queuing Delay and Schedule Delay in LMINET2
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In Figures 11 and 12, the bars represent the delay figures corresponding to the specified weather days in 2025. In Figure 11, the average flight queuing delays and the schedule delays are compared for all the commercial flights in the NAS. One can see that the schedule delays are about half a minute more than their respective queuing delays, which result from the complex mechanisms of delay propagation, ground turnaround time, and schedule padding. Whereas the queuing and schedule delays in Figure 11 are both based on running the same LMINET2 model, Figure 12 compares the average flight queuing and schedule delays under the hypothetical assumption that the there is no flight delay propagation and cancellation versus the flight schedule delays in a normal connected model. The assumption of no flight connection implies that the late arrival does not cause late departure in the next flight leg. Everything else in the two models compared in Figure 12 is unchanged including the calculation of flight schedule delay from queuing delay, schedule padding, and departure delay adjustment. One can see that the queuing and schedule delays in the unconnected model are very close—a reflection of the parameter setting of model, but the schedule delay in the connected model is about half a minute more that the delays in the unconnected model. Since the old LMINET is just a special case of LMINET2 under the assumption of an unconnected model and for the queuing delay, we can say the delays reported by LMINET under-estimate the average flight delays by about half a minute. It should be noted that the differences between in the delays identified in Figures 11 and 12 are just for this particular scenario: demand, capacity, and weather, which should not be generalized. We believe the consistent differences are the result of the large increases in NAS capacities and the increases in the bad weather in particular, which tend to be more than the ones in good weather. In other words, there is no massive disruption of NAS operations in the particular 7 days selected.
VI. Summary
In this paper, we have presented a NAS-wide operations simulation model, and one application to quantify the flight delay benefits under NextGen. The model takes a comprehensive view of airport operations including runway and taxiway delays, ground turn around, delay propagation and absorption, flight cancellation, and ground delay programs. Both the queuing delays and the schedule delays match closely to the traffic statistics collected by the ASPM. Based on our validation results, we believe it is a reasonable and reliable model that can be set up and run quickly and that can be applied in many ATM technology benefit applications.
The model, however, is still missing one important module to cover the airspace operations. Although not the bottleneck on good weather days in the current NAS, the airspace may be congested in the future with the increased traffic, and bad weather can cause havoc on NAS operations. This is why many proposed NextGen technologies are to minimize the weather impact. The future enhancement of LMINET2 will be mainly in this area.
Acknowledgements
While the design and development of LMINET were undertaken with LMI independent research and development funds, the authors would like to thank Yuri Gawdiak of NASA/JPDO for sponsoring the work of applying LMINET2 to NextGen analysis.
References
1. Long, D., et al. A Method for Evaluating Air Carrier Operational Strategies and Forecasting Air Traffic with Flight Delay, Logistics Management Institute, November, 1999.
2. Long, D., et al., Upgrading LMINET — A Queuing Network Model of the National Airspace System, Logistics Management Institute, February, 2002.
3. Long, D., J. Eckhause, and S. Hasan, Using Enabled Throughput Instead of Reduced Delay to Quantify Capacity Improvement Benefits, AIAA 3rd ATIO Conference, Denver, CO., November, 2003.
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4. Long, D., J. Hees, and S. Hasan, Benefits of Distributed Air/Ground Traffic Management (DAG-TM) Concepts 5 and 6 During Off-Nominal Conditions, AIAA 5th ATIO Conference, Arlington, VA, September 2005.
5. Long, D., et al., A Small Aircraft Transportation System (SATS) Demand Model, LMI Report NS004S1, January 2001.
6. Department of Transportation, FAA Aerospace Forecasts—Fiscal Years 2007–2020, Federal Aviation Administration, Office of Aviation Policy and Plans, Statistics and Forecast Branch, Washington, D.C., March 2007.
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