Evaluating Airfield Capacity (2012)

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47 New Airfield Capacity Evaluation Tools and Guidance One of the main objectives of this ACRP research project was to identify gaps in existing models and recommend new or enhanced models to fill those gaps. Based on the review of the existing models discussed in Chapter 3, two major gaps were identified: 1. Level 1 and Level 2 methods that provide the flexibility for the user to input assumptions that differ from those used to create the existing Level 1 and 2 tables and charts/nomographs in order to better represent the user’s specific conditions 2. A Level 4 capacity simulation model for estimating the maximum sustainable throughput of complex airfield layouts that is specifically designed for that purpose and available to the public This chapter addresses the limitations of existing modeling techniques by introducing and reviewing new and newly available modeling tools. In addition, this chapter provides guidance for taking into account factors that influence airfield capacity but typically are not directly con- sidered in existing airfield capacity models. Overview of New and Newly Available Models The new and newly available modeling tools shown in Figure 4-1 are described in this section in terms of how they differ from existing models, their recommended applications, their limita- tions, and recommended further development. The new spreadsheet-based model encompasses the capabilities of the existing capacity estima- tion techniques at Levels 1 and 2, but has much greater flexibility for inputting case-specific assump- tions to represent the user’s unique conditions. Moreover, the Level 3 spreadsheet is much more user-friendly and transparent than the existing FAA Airfield Capacity Model (ACM); however, it will require further development to encompass all runway use configurations currently included in the ACM. The newly available Level 4 model, the runwaySimulator, which the MITRE Corporation plans to make publicly available in 2012, has been reviewed and validated. This model is expected to become the “model of choice” for evaluating the capacity of complex airfields. The Level 5 models are outside the scope of this guidebook but it is expected that they will continue to be used as they are today. New Prototype Airfield Capacity Spreadsheet Model (Levels 1, 2, and 3) A new prototype set of Excel spreadsheets was developed as part of this research project. This prototype modeling tool is intended to help airport planners understand and determine airfield capacity at a higher fidelity than AC 150/5060-5, Airport Capacity and Delay (the AC), but with C h a p t e r 4

48 evaluating airfield Capacity much less effort than required to apply aircraft delay simulation models like the Simulation Model (SIMMOD) and Total Airspace and Airport Modeler (TAAM). The new spreadsheet model, here referred to as the Airfield Capacity Spreadsheet Model, should be considered a prototype. Capabilities/Description For many years, the ability to quickly estimate airfield operational capacity has been limited to rules of thumb for simple configurations and a lookup table provided in the AC. The Airfield Capacity Spreadsheet Model was developed as an intermediary between the lookup tables avail- able in the AC (some of which are replicated using new calculations in the spreadsheet model) and the Level 4 and 5 simulations. The Airfield Capacity Spreadsheet Model is built on base calculations following the method- ology in the ACM program and applies variable separation, spacing, and clearance standards following the guidelines included in FAA JO 7110.65, Air Traffic Control, and FAA EM-78-8A, Parameters of Future ATC Systems Relating to Airport Capacity/Delay. FAA’s ACM is discussed in detail in FAA RD-76-128, Reference 1, Model User’s Manual for Airfield Capacity and Delay Models. These references are further described in Appendix B. The Airfield Capacity Spreadsheet Model is designed to be a working planning tool, similar to the ACM but with more flexibility to change input assumptions to represent site-specific condi- tions from the most simple airfield configurations to moderate airfield configurations. Improvements and Differences from Previous Models The Airfield Capacity Spreadsheet Model is intended to serve as a beginning-level capacity cal- culation option. For cases where a detailed analysis is not warranted, either because of budgetary or airport conditions, this new model can provide timely and accurate airfield capacity estimates for simple to moderately complex airfields. The model is not intended to be used for complex airfield configurations or when a higher degree of specificity is required (e.g., to support large- scale airfield redevelopment projects or highly controversial capacity projects). Source: ACRP Research Team. Figure 4-1. Overview of relationship between existing and new models.

New airfield Capacity evaluation tools and Guidance 49 The Airfield Capacity Spreadsheet Model can be used when limited data are available. Many of the base default parameters that can be used for simple single or dual runway airfields without significant unique restrictions are contained in the spreadsheet model. Checklists provided in this guidebook can help determine the level of data available for the modeling task anticipated and the level of modeling sophistication that can be achieved with the available data. The new spreadsheet model also includes a simplified tab similar to the table lookups in the AC but focus- ing only on the airfield configurations currently in the Airfield Capacity Spreadsheet Model. The spreadsheet model user is assumed to have limited knowledge of air traffic control (ATC) or FAA rules and guidelines on air traffic and pilot procedures regarding approaches and depar- tures. The explanations provided in the Capacity Spreadsheet Model User’s Manual in Appendix A of this guidebook are intended to provide sufficient understanding for planners to successfully use the new model, although they do not provide sufficient detail to serve as a tutorial on airfield capacity planning. Recommended Uses/Applications The Airfield Capacity Spreadsheet Model is most applicable to small to midsized airports, airports without complex airfield layouts, and airports for which a detailed capacity analysis is unnecessary. At present, the Airfield Capacity Spreadsheet Model can be used to calculate average hourly capacity levels only for the following general airfield configurations: • Single runway • Dual parallel runways • Dual intersecting runways Each general configuration can be uniquely adjusted to closely fit the conditions of the user’s specific airfield through selected input parameters. The following parameters can be modified in the Airfield Capacity Spreadsheet Model to estimate the effect on resulting airfield capacity: • Aircraft fleet mix • Visual meteorological conditions (VMC) versus Instrument meteorological conditions (IMC) • Arrival runway occupancy time • Average aircraft approach speeds • Runway exit availability • Type of parallel taxiway (full, partial, or none) • Availability of an air traffic control tower (ATCT) • Runway crossings • Percent of touch-and-go activity • Length of common approach • Departure-arrival separation • Arrival gap spacing buffer • Departure hold buffer • Arrival-arrival separation requirements • Departure-departure separation requirements Changes can be made to the defaults in the model to test the results of the various parameters listed above. Airfield Capacity Spreadsheet Model—Sample Results for Levels 1, 2, and 3 Outputs from the Airfield Capacity Spreadsheet Model can be used as Level 1, Level 2, or Level 3 modeling results.

50 evaluating airfield Capacity For Level 1, the model file includes an example of a new lookup table that presents five airfield configurations comparable to those in the AC. Figure 4-2 presents a screen shot of a lookup table generated using the Airfield Capacity Spreadsheet Model. This example incorporates capacity estimates developed with the listed assumptions in Chapters 1 and 2 of the AC and follows the general guidelines in the User’s Guide in Appendix A of this guidebook. In the Level 1 method, the user only makes an assumption regarding the fleet mix that most closely represents the actual aircraft fleet mix at the airport in question, and reads the hourly capacity and annual service volume (ASV) estimates for the comparable airfield configuration. If desired, the user can use the Airfield Capacity Spreadsheet Model Level 2 and 3 methods to assess the capacity of a more specific fleet mix. The lines between Level 2 and Level 3 modeling become blurred when considering the Airfield Capacity Spreadsheet Model, because the spreadsheet model is essentially both a Level 2 and Level 3 model in one tool. If the user relies only on the default assumptions in the model, it more closely resembles a Level 2 application. If the user takes advantage of the advanced features of the model, it more closely resembles a Level 3 application. Moreover, the Level 2 portion of the model yields simplified arrival and departure priority capacities as outputs, which the user must combine to estimate a total hourly capacity. By apply- ing the advanced features (or Level 3) of the model, the user can achieve a more realistic and balanced capacity result. An example of a Level 2 model result for a single-runway configuration is shown on Figure 4-3. The Level 2 output for VMC in this example shows the hourly arrival-priority capacity to be a mix of 33 arrivals and 14 departures. The departures-only hourly capacity is shown to be 52 departures. In VMC, the default hourly arrival-priority capacity, based on the default settings and the user-specified fleet mix and airfield conditions, results in an operational mix of 70% (33) arrivals and 30% (14) departures, for a total of 47 operations, which is the maximum hourly capacity in a mixed operations situation. In the example given in Figure 4-3, notice that the hourly IMC total mixed operations capacity of 54 operations is greater than the hourly VMC total mixed operations capacity of 47 opera- tions. When such an anomalous result occurs, it is because the larger separation requirements between arrivals in IMC allow more aircraft to depart in the gaps between arrivals. More impor- tantly, it is a clear indication that the user must gap arrivals to let aircraft depart to increase VMC capacity, because any operations that can take place in IMC can take place in VMC. Such gapping is allowed in the Level 3 application of the Airfield Capacity Spreadsheet Model. In the Level 3 environment, the user could adjust the aircraft performance parameters and make use of the advanced features of the model to arrive at an appropriate and balanced hourly capacity with 50% arrivals and 50% departures. Adjusting the gap spacing buffer in Level 3 is the primary mechanism used to achieve a balanced or specific operations mix. Figure 4-4 illustrates the output for the same single-runway configuration used for the Level 2 example, but with Level 3 inputs adjusted to balance the operations mix between arrivals and departures. The Level 3 output for VMC in this example shows the arrival-priority capacity to be 33 arriv- als with the departures-only capacity still shown to have an hourly capacity of 52. The balanced mix is essentially 28 arrivals and 27 departures, or 55 total operations, which results because the gap spacing is adjusted to allow for more departures between arrivals. Also note that the VMC capacity of 55 is now greater than the IMC capacity of 52, as appropriate.

New airfield Capacity evaluation tools and Guidance 51 Source: Landrum & Brown. Figure 4-2. Level 1 model example—sample lookup table.

52 evaluating airfield Capacity Source: Landrum & Brown. Figure 4-3. Level 2 model example—default outputs. Figure 4-4. Level 3 model example—balanced outputs. Source: Landrum & Brown.

New airfield Capacity evaluation tools and Guidance 53 Testing and Validating the Airfield Capacity Spreadsheet Model During the development of the Airfield Capacity Spreadsheet Model, comparison testing was conducted against actual data from FAA’s Aviation System Performance Metrics (ASPM) data- base and capacity estimates and the lookup tables in the 2012 Capacity Benchmark Report, and runwaySimulator. Throughout the process of validating the spreadsheet model, assumptions were adjusted and features were added to the calculation process to resolve some of the differ- ences between the capacity estimates derived for the test airports using the spreadsheet model and those derived using the other methods. Several scenario test cases were modeled and the results were compared against single, dual par- allel, and intersecting runway configurations at San Diego International Airport, Fort Lauderdale- Hollywood International Airport, and LaGuardia Airport, respectively. The testing process required the use of consistent input parameters based on data from the ASPM database, ATCT data, and assumptions on operational practices based on knowledge of the airport operations. Results of this testing effort are shown in Table 4-1. Airport RunwayLayout Capacity Es�ma�on Method Fleet Mix Maximum Hourly Capacity VMC IMC San Diego Interna�onal Airport (SAN) Single runway AC 150/5060-5 81-120% (C+3D) 55 53 2012 Benchmark N/A 57 48 runwaySimulator 4% Small-S, 20% Small+, 67% Large-Jet, 9% Heavy 60 52 Airfield Capacity Spreadsheet Model 4% Small-S, 20% Small+, 67% Large-Jet, 9% Heavy 60 54 Mineta San José Interna�onal Airport (SJC)* Closely spaced dual parallel runways AC 150/5060-5 81% to 120% (C+3D) 105 59 2012 Benchmark N/A N/A N/A runwaySimulator 18% Small-S, 3% Small-T,76% Large-Jet, 3% Heavy 68 50 Airfield Capacity Spreadsheet Model 18% Small-S, 3% Small-T, 76% Large-Jet, 3% Heavy 68 48 New York LaGuardia Interna�onal Airport (LGA) Intersec�ng runways AC 150/5060-5 81% to 120% (C+3D) 76 59 2012 Benchmark N/A 86 74 runwaySimulator 4% Small-S, 20% Small+, 67% Large-Jet, 9% Heavy 76 64 Airfield Capacity Spreadsheet Model 4% Small-S, 20% Small+, 67% Large-Jet, 9% Heavy 78 66 Fort Lauderdale– Hollywood Interna�onal Airport (FLL) † Dual independent parallel runways AC 150/5060-5 81% to 120% (C+3D) 111 70 2012 Benchmark N/A 74 56 runwaySimulator 4% Small-S, 20% Small+, 67% Large-Jet, 9% Heavy 68 52 Airfield Capacity Spreadsheet Model 4% Small-S, 20% Small+, 67% Large-Jet, 9% Heavy 64 56 * Considers only Runway 12L-30R and Runway 12R-30L; Runway 11-29 not considered in analysis. † Considers exis�ng runway configura�on of one runway open to all aircra� types and one runway used by small aircra� only. Table 4-1. Commercial airport model result comparison.

54 evaluating airfield Capacity Validation of the Airfield Capacity Spreadsheet Model outputs showed acceptable and com- parable results in hourly capacity between the spreadsheet model and runwaySimulator (e.g., 62 operations per hour versus 59 operations per hour is regarded as an acceptable variation). The spreadsheet model produces an output that is more in line with a maximum capacity because the calculations are based on minimum separations. The results from the model can be adjusted or reduced by approximately 10%, as in the runwaySimulator observations, to represent typical actual hourly flow rates that occur in a busy or peak period. The Airfield Capacity Spreadsheet Model outputs also were compared to the AC lookup table in five cases, as summarized in Table 4-2. A new sample lookup table in the model was prepared using the assumptions outlined in the advisory circular as much as possible for a valid com- parison. The biggest differences were in the IMC/IFR calculated results, where the spreadsheet model results were typically 10% higher (and more than 20% higher in a few instances) than the capacity values in the AC. In summary, the hourly capacity counts were within the “10% or less” variance range and in many cases were nearly the same. The differing results may be attributed, in part, to the fleet mix allocation chosen for the spreadsheet model to represent five standard cases of varying aircraft types. The AC provides a range of fleet mix percentages but does not specify the actual fleet mix, whereas the spreadsheet model uses a specific set of fleet mix percentages. As such, the varying results observed are considered to be acceptable given the possible differences in assumptions and fleet mix specifications. Overall, during testing the results from the Airfield Capacity Spreadsheet Model compared favorably with the other capacity estimates, and were found to be within a reasonable range. The variances can be understood in terms of the potential differences that could result from how the input assumptions are specified in each methodology. The Airfield Capacity Spreadsheet Model is presented as a prototype and, with future development, could be improved to provide the user even more ability to customize inputs so that it would also apply to more complex airfields. Limitations Although the spreadsheet model provides for significant input flexibility for a variety of param- eters, as noted previously, if the airfield configuration is not included in the model or the airfield is operated in many different configurations, then the Airfield Capacity Spreadsheet Model would not Table 4-2. Small airport model result comparison. Airport Runway Layout Percent Touch-and-Go Opera�ons Capacity Es�ma�on Method Fleet Mix Maximum Hourly Capacity VMC IMC Small recrea�onal airport Single runway 50% AC 150/5060-5 0% to 20% (C+3D) 98 59 Airfield Capacity Spreadsheet Model 100% Small-S 90 66 Small execu�ve airport Single runway 40% AC 150/5060-5 21% to 50% (C+3D) 74 57 Airfield Capacity Spreadsheet Model 25% Small-S, 50% Small-T, 25% Small+ 74 62

New airfield Capacity evaluation tools and Guidance 55 reflect a total combined hourly capacity. The model’s results present the following information for VMC, IMC, and an average weather condition: • Arrivals-only capacity (with and without touch-and-go activity) • Departures-only capacity (with and without touch-and-go activity) • Total mixed operations The spreadsheet model does not directly allow for the results to be combined to reflect the capaci- ties for different arrival-departure ratios (or percentages of arrivals) over the course of the day. The Airfield Capacity Spreadsheet Model also does not allow some of the features of detailed simulation modeling, such as importing flight schedules or ASPM data. Suggestions for Further Work The Airfield Capacity Spreadsheet Model presents a first step toward a simplified, more trans- parent version of the ACM with more flexibility than currently provided by the methodologies in the AC. With additional resources, the spreadsheet model could be expanded to allow for additional user inputs to depict more airfield operational conditions. It should be noted that a more detailed version of the spreadsheet model would also require the user to input significantly more data and have more knowledge of the airfield’s operating conditions. Newly Available Level 4 Model—The MITRE runwaySimulator The Level 4 model examined in this research project was the MITRE runwaySimulator (Figure 4-5). This level of sophistication reflects models that provide the flexibility of simulation but are easier to use and are intended to estimate throughput capacity rather than aircraft delay. Source: MITRE Corporation. Figure 4-5. Schematic of runwaySimulator’s components.

56 evaluating airfield Capacity The runwaySimulator is rapidly becoming FAA’s model of choice for evaluating current airfield capacity. For example, MITRE is using the model in updating (1) FAA’s Airport Capacity Bench- mark Report (to be released in 2012), and (2) the Future Airport Capacity Task 3 (FACT 3) Study, “Capacity Needs in the National Airspace System: An Analysis of Airport and Metropolitan Area Demand and Operational Capacity in the Future.” The 2010 version of runwaySimulator was examined for purposes of this guidebook. The model is currently being reprogrammed in Java, primarily to improve usability and the user interface. It is expected that the 2012 version of runwaySimulator will be more user-friendly and add some features while keeping the same core logic and functionality. The new version of runwaySimulator is expected to be made publicly available in 2012. Capabilities/Description The runwaySimulator software runs on a personal computer (PC) running Microsoft Win- dows and currently requires runtime licenses for SLX and Proof, available from Wolverine Software Corporation, which is used by the simulation engine. MITRE plans to eliminate this requirement for the separate SLX and Proof runtime licenses in the publicly available version of runwaySimulator that is expected to be released later in 2012. The runwaySimulator has a graphical user interface (GUI) for users to enter inputs. Results can be viewed within the model, and can also be exported to Excel. Outputs include a Pareto frontier (arrival priority, departure priority, and balanced capacity), as well as runway use and throughput by aircraft type. The runwaySimulator has a graphical module to enable viewing of an animation of the runway operations. The runwaySimulator has capabilities to import some input data, including runway layout, aircraft performance, and ATC separations, which reduce the data collection and reduction bur- den. However, the user must verify that certain default data are applicable to the local airfield layout and operating conditions. Improvements and Differences from Previous Models The runwaySimulator model is designed to fill the gap between high-level analytical models and detailed aircraft delay simulation models like SIMMOD and TAAM. The runwaySimulator provides the capability to estimate capacity at airports with complex airfields and unique oper- ating procedures. Previously, the only capacity estimation method that could reliably account for specific runway dependencies, runway use restrictions, and close-in airspace constraints was a detailed simulation model designed to estimate delay (i.e., SIMMOD or TAAM). The run- waySimulator reflects more unique operating procedures without the level of effort and input assumptions required for Level 5 aircraft delay simulation. Recommended Uses/Applications The runwaySimulator provides for a Level 4 tool to estimate maximum sustainable throughput of complex airfield layouts. It was specifically designed for that purpose and future versions will be made available to the public. Therefore, the runwaySimulator is recommended for use in estimating the hourly throughput capacity of a complex airfield, or in estimating the capacity for an airport with complex operating procedures. In particular, the runwaySimulator should be selected over Level 3 or lower models when estimating capacity for airports having the following characteristics: • Runway configurations that are not represented in the set of configurations available in the ACM, the AC, or the Airfield Capacity Spreadsheet Model

New airfield Capacity evaluation tools and Guidance 57 • Unique runway dependencies, approach procedures, or departure procedures • Runways that can only be used by certain aircraft types because of runway length or noise abatement policies • Limited departure fixes or headings that restrict operations • Unique approach procedures that involve nonstandard dependencies between runway operations, such as Simultaneous Offset Instrument Approach (SOIA), Converging Runway Display Aid (CRDA), and others Testing and Validating the runwaySimulator The purpose of testing and validating the runwaySimulator was to assess the reliability of its capacity estimates. The research team applied the model to 14 test cases, each defined in terms of an airport, runway configuration, and weather condition. Four large commercial airports were represented—Newark Liberty International Airport (EWR), LaGuardia Airport (LGA), San Diego International Airport (SAN), and Fort Lauderdale-Hollywood International Airport (FLL). These airports provide representative, busy, one- and two-runway airports where demand is at or near capacity during peak hours, but are less busy during other hours. They also provide a sampling of dual parallel and intersecting runway conditions, and include aircraft taxiing across runways. Three visibility conditions—VMC, Marginal VMC (MVMC), and IMC—are repre- sented for each airport. Two different VMC configurations were tested for EWR and LGA, bring- ing the total tests to 14. In these test cases, the research team compared the runwaySimulator outputs with observed data. The source of the observed data was FAA’s ASPM database. ASPM contains extensive opera- tional data for major U.S. airports, including all four considered in the tests. Much of the ASPM data is aggregated into quarter-hour observations, by airport; such quarter-hour data include: • Runway configuration • Arrival and departure counts • Called rates—airport arrival rates (AARs) and airport departure rates (ADRs) • Cloud ceiling and visibility • Estimates, based on flight plans and actual arrival and departure times, of arrival and departure demand In addition to these quarter-hour data, the ASPM database includes data on individual flights, including departure and arrival airports, out-off-on-in (OOOI) times, scheduled arrival and depar- ture times, and aircraft type. For this research project, individual flight data determined the fleet mix. The research team obtained the aforementioned data for each of the four airports consid- ered in the test cases, covering the 5-year period from August 1, 2006, through July 31, 2011. These data were then filtered to obtain observations that would be suitable for comparison with runwaySimulator output. For a given test case, the first step was to collect the observed counts and called rates for the associated airport, runway configuration, and weather condition. Next, the quarter-hour observed counts were filtered and aggregated to obtain a set of hourly observa- tions that met the following criteria: • Throughout the hour, the sum of the AAR and the ADR was within the normal range observed for that airport over the 5-year period. • Demand throughout the hour was sufficiently high to justify the assumption that the airport was operating at or near capacity. • The fleet mix among the cases was fairly consistent. (Among the sets of observations with similar fleet mixes, the set with the largest number of hourly observations was selected as the aircraft fleet mix for the test case.) • Wind conditions did not appear to significantly reduce throughput.

58 evaluating airfield Capacity The hourly observations that resulted from this procedure were expected to reflect situations in which (1) demand at the airport was sufficient for it to be considered operating at or near capacity, and (2) the aircraft fleet mix was fairly consistent. Substantial variability in hourly counts was still observed, however. To facilitate comparisons with the runwaySimulator output, the observed counts were clus- tered. A centroid was calculated for each cluster by averaging the arrival count and departure count for each cluster member. The objective was to identify clusters with centroids that reflected the realized capacity for the associated airport, configuration, weather condition, and fleet mix. The identified centroids were expected to be close to estimated capacities as represented by the Pareto curve obtained from the runwaySimulator output, with inputs for the same conditions. This analysis produced a series of plots, one for each test case. Figures 4-6 through 4-9 show plots for some of the test cases. Each plot contains the Pareto curve from the runwaySimulator, along with observed counts from ASPM (after the filtering and aggregation process described above), cluster centroids derived from the observed counts, and the called rates (AARs and ADRs) for the given test case. In some cases, such as EWR-IMC-4R|4L (Figure 4-6) and LGA-VMC-4|13 (Figure 4-7), there is good agreement between the cluster centroids and the modeled Pareto curves. In other cases, such as FLL-VMC-9L,9R|9L,9R (Figure 4-9) and SAN-VMC-27|27 (Figure 4-8), observed counts were lower than modeled capacity. The cases in which capacity exceeds observed throughput can be further subdivided based on the called rates. In the case of SAN-VMC-27|27, the called rate appears to be close to the Pareto curve defined by the cluster centroids. It appears that, in this case, throughput is limited by the called rate rather than capacity. On the other hand, in the case of FLL-VMC-9L,9R|9L,9R, the most common called rates are well outside the Pareto curve while the observed counts are well inside and far below the called rates. This result probably reflects a situation in which throughput is truly demand limited. Source: University of California, Berkeley. Newark Liberty International Airport, IMC Arrive Runway 04R, Depart Runway 04L 0 5 10 15 20 25 30 35 40 45 50 55 0 5 10 15 20 25 30 35 40 45 50 55 A rr iv al s Departures Pure Arrival Priority Pure Departure Priority Observed Observed Cluster Called Rates Figure 4-6. Capacity comparisons for Newark Liberty International Airport.

New airfield Capacity evaluation tools and Guidance 59 Source: University of California, Berkeley. 0 10 20 30 40 50 60 0 10 20 30 40 50 60 A rr iv al s Departures LaGuardia Airport, VMC Arrive Runway 04, Depart Runway 13 Pure Arrival Priority Pure Departure Priority Observed Observed Cluster Called Rates Figure 4-7. Capacity comparisons for LaGuardia Airport. Source: University of California, Berkeley. 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 A rr iv al s Departures San Diego International Airport, VMC Arrive Runway 27, Depart Runway 27 Pure Arrival Priority Pure Departure Priority Observed Observed Cluster Called Rates Figure 4-8. Capacity comparisons for San Diego International Airport.

60 evaluating airfield Capacity Overall, the results of the analysis revealed that realized counts, even in periods of relatively high demand, were generally lower than the capacities estimated by the runwaySimulator. To quantify this difference, the research team examined each selected cluster centroid and compared the total operations for that point with those for the point on the runwaySimulator-generated Pareto curve with the same mix of arrivals and departures. Out of a total of 27 centroids, 24 had fewer operations than their associated Pareto points. The average centroid-to-Pareto point ratio was 0.84, with a range from 0.66 to 1.05. These ratios suggest that, as a rule of thumb, about 15% should be subtracted from the runwaySimulator results to approximate operational counts that will be consistently realized in periods of high demand. The above results indicate the difficulty of finding empirical observations in which through- put is truly limited by airfield capacity and, therefore, is an accurate representation of airfield capacity. This finding motivated an alternative approach to comparing the count data with runwaySimulator results based on a statistical technique called censored regression. As applied in the research that led to this guidebook, censored regression is based on three main ideas: 1. Observed throughput is the minimum of demand and realizable capacity. 2. Realizable capacity is a random variable that varies significantly from time period to time period, even for a given configuration and visibility condition, because of changes in other factors, such as aircraft fleet mix, the arrival-departure split, and airspace fix/runway loadings. 3. The mean realizable capacity increases with demand, reaching full capacity only as demand becomes very high. The first idea is widely accepted and central to the concept of capacity. The second and third ideas are more specific to airfield capacity and the ASPM demand metrics used in this research project. The ASPM data reveal that, for any given demand level, there is a substantial dispersion in throughput. As ASPM demand increases, the average throughput is observed to increase in a fairly continuous manner, albeit at a decreasing rate. This is most likely because ASPM demand Source: University of California, Berkeley. 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 A rr iv al s Departures Fort Lauderdale-Hollywood International Airport, VMC, Arrive and Depart Runways 09L/R Pure Arrival Priority Pure Departure Priority Observed Observed Cluster Called Rates Figure 4-9. Capacity comparisons for Fort Lauderdale–Hollywood International Airport.

New airfield Capacity evaluation tools and Guidance 61 is based on flight plans and, because of upstream disturbances, some demand does not material- ize at the anticipated time. Demand must, therefore, be very high for it to be certain that enough flights will actually be available to arrive or depart during any given time period in order to make full use of the available capacity. Based on the above assumptions and using the ASPM count and demand data—including observations in which demand was quite low—the research team developed a statistical model that estimates the limiting capacity that is realized when demand becomes very high. The coef- ficients of the model were estimated using the data for one VMC case and one IMC case for each of the four test-case airports, or eight test cases in all. This estimation approach took into account that operational capacity varies with arrival/departure mix and is typically greatest when the mix is about even. The estimated limiting capacity values were then compared with the corresponding values obtained from runwaySimulator. Highlights of the results include the following: • Capacities estimated using the statistical model averaged about four operations per hour, or 5% greater than runwaySimulator predictions. • Considering all eight test cases, the mean absolute error was ten operations per hour. Exclud- ing FLL, which had poor results because of limited fleet mix data, the error decreased to seven operations per hour. • The correlation between the statistically estimated and runwaySimulator-generated capacities was 0.79 including FLL and 0.87 excluding FLL. In conclusion, observed counts, even during busy periods featuring typical operating condi- tions, are often generally below capacities calculated by runwaySimulator. This difference does not mean that runwaySimulator is wrong. Rather, it means that airfield capacity is not the only factor limiting realized throughput as observable in the ASPM data on quarter-hour and hourly throughput values. As a rule of thumb, capacities calculated using runwaySimulator should be reduced by 15% to approximate the actual counts that are representative of typical busy periods. At the same time, a statistical model that uses observed counts to predict throughput in very busy periods yields results that are fairly close and, in fact, generally exceed the capacity esti- mates from runwaySimulator. In this latter case, it can be said that the model is consistent with the data that most closely approximate what the research team generally considers to be most representative of maximum sustainable throughput: namely, the capacities estimated using the statistical model. Limitations/Suggestions for Further Work It should be noted that runwaySimulator 2010 was used for purposes of this research project. Some of the limitations discussed in this section may be resolved or improved with the release of runwaySimulator 2012, which MITRE intends to make available to the public. The runwaySimulator installation process involves many steps, and the instructions provided do not identify all the possible problems the user may encounter during installation. Therefore, it may be difficult for the new user to complete the installation without assistance. Operation of runwaySimulator requires a trained user with sophisticated knowledge of the airport and operating procedures and with experience running computer simulation models. Employing all features in runwaySimulator requires a user who also is knowledgeable about air- field and airspace operations and ATC procedures. New users require substantial training to use the model properly, even if they are familiar with other capacity evaluation techniques. The runwaySimulator requires much more site-specific input data and assumptions than the Level 3 models. This data burden is somewhat reduced, given the ability to import runway

62 evaluating airfield Capacity geometry, aircraft performance, and aircraft separation data. However, these data must be veri- fied to confirm that they reflect actual operating procedures at the airport. Because the model is fairly data-intensive, its proper use requires substantial coordination with the airport opera- tions and air traffic specialists to fully understand the operations and dependencies. The runwaySimulator does not currently simulate landing-roll behavior. Instead, arrival run- way occupancy times (AROTs) are a model input by aircraft type, and are applied based on a user-entered standard deviation. To more accurately depict the influence of runway exit loca- tions on runway occupancy times, it would be preferable to incorporate a landing-roll simulation module within the model. Until recently, runwaySimulator 2010 had two modes for modeling AROTs. The first was to use an underlying trajectory landing-roll model, which incorporates aircraft performance parameters for touchdown speed, deceleration, and exit speed. The second was to draw an AROT from a user-specified distribution and to re-compute the deceleration parameter that would realize it. Both modes had arrivals exiting at modeled exit locations. The version of runwaySimulator tested in this research uses a single mode whereby only the drawn AROT applies and exit locations are ignored. The anticipated 2012 version of runwaySimulator will support the two modes again: (1) the underlying trajectory landing-roll model, which will use exits, and (2) drawn AROTs, which will not. Finally, the runwaySimulator is not yet very user-friendly; its user interface is complex, and its input stream is not transparent. It would be helpful if the user could easily review and make changes to the inputs. It is not always clear to new users when they can simply save the changes in the settings and when they must rerun the automated rule-generation feature referred to as the “Xbox.” In addition, users may hesitate to change the settings because not enough instruction is given on the consequence of the changes. Documentation supporting the runwaySimulator is limited. It is recommended that a detailed user’s manual be developed to accompany the 2012 version of the model when it is made publicly available. New Guidance on Specialty Cases Every airport has unique considerations that are important to reflect in a capacity analysis but may be challenging to account for explicitly in the available capacity analysis tools. It is often necessary to make adjustments outside of the model to reflect these considerations. More specific guidance is provided below for five specific situations that are commonly encountered: 1. Absence of a full-length parallel taxiway 2. Effects of runway crossings 3. Effects of an ATCT 4. Effects of staggered runway thresholds 5. Effects of aircraft-specific runway use restrictions Absence of a Full-Length Parallel Taxiway In the absence of parallel taxiway and connecting runway exits, aircraft must spend exces- sive time on the runway, which greatly reduces the effective capacity of the runway. The precise reduction depends on the presence or absence of intermediate taxiways that pilots can use to either access or exit the runway. Therefore, the effect on capacity is very site-specific. In such circumstances, runway capacity is generally driven entirely by the required runway occupancy times of the aircraft operating on the runway. These runway occupancy times can be defined as follows:

New airfield Capacity evaluation tools and Guidance 63 • AROT begins when an arriving aircraft passes over the runway threshold and ends when it exits the runway. Without an available parallel taxiway, AROT includes time for the aircraft to taxi to the end of the runway, turn around, and taxi back on the runway until it reaches one of the centrally located taxiways leading to the aircraft parking ramp. • DROT (departure runway occupancy time) begins when a departing aircraft begins to taxi to the end of the runway and includes the time it takes for the aircraft to turn around, complete its takeoff roll along the runway, and clear the opposite end of the runway. Typical runway occupancy times are in the range of 40 to 60 seconds for arrivals and 30 to 45 seconds for departures. With no parallel taxiway, however, runway occupancy times can be as long as 4 to 6 minutes, depending on the locations where aircraft access and exit the runway and the location of the ramp. In extreme situations, the airfield capacity of a runway with no paral- lel taxiway can be as little as 15 to 20 operations per hour even when one or two intermediate entrances/exit points connect the runway to the ramp. Unusually long runway occupancy times can be entered into any Level 2 through Level 5 ana- lytical or simulation model that accepts AROTs and DROTs as inputs. For example, the effects of long runway occupancy times presented in Table 4-3 were estimated using the ACM. The table shows example calculations for estimating airfield capacity based on measured values of AROTs and DROTs for a hypothetical single runway with no parallel taxiway and one intermediate con- nection between the runway and the ramp. These long runway occupancy times are the primary determinant of hourly runway capac- ity, and their effects on capacity also can be estimated outside of any model, using the tabular approach described in the notes following Table 4-3. The Airfield Capacity Spreadsheet Model allows for up to a 50% reduction in estimated capacity where there is a partial taxiway or no parallel taxiway (see Figure 4-10). Essentially, the calculated capacity in the model may be halved if the runway must also be used as a taxiway for Air Carrier and Air Cargo Aircra� Opera�ons Runway Occupancy Times (minutes) Hourly Runway Capacity Runway Configura�on Arrivals Departures Length of Arrival- Departure Cycle Flow direc�on A 6.0 2.0 8.0 15 Flow direc�on B 1.5 5.5 7.0 17 (1) Measure the approximate aircra� taxiing distance between comple�on of its landing rollout and exi�ng the runway, or between entering the runway and the beginning of its takeoff roll. (2) Convert that distance into taxiing �me by dividing the taxiing distance derived in Step 1 above by a reasonable average taxiing speed (e.g., 15 to 20 miles per hour). (3) Add to the taxiing �mes derived in step 2 above either the landing rollout �me (about 40 to 50 seconds) for arrivals, or runway clearance �me (approximately 30 to 40 seconds) for departures to obtain the total AROT or DROT for each type of movement. (4) Add the resul�ng runway occupancy �mes for arrivals and departures together to obtain a total arrival-departure cycle �me (in minutes). (5) Obtain the hourly runway capacity es�mate by dividing the total arrival-departure cycle �me into 60 minutes per hour, then mul�plying by 2 to derive the total arrival and departure capacity, as shown in the right-hand column. Source: LeighFisher. Table 4-3. Estimated hourly runway capacities with no or partial parallel taxiway (sample calculations).

64 evaluating airfield Capacity the clearance of arrivals and departures. A factor of 1 is used with a full parallel taxiway, a factor of 0.7 is used with a partial taxiway, and a factor of 0.5 is used when no parallel taxiway exists. The model makes use of this reduction factor only in the single-runway configuration; otherwise, it is assumed that an adequate taxiway system is standard for dual parallel-runway systems or larger. In addition, in the single-runway configuration model, the user can either (1) use the default runway occupancy times and select the type of parallel taxiway (which triggers the reduction factor), or (2) apply calculated or known runway occupancy times that result from not having a full-length parallel taxiway, which would alleviate the need to apply the capacity reduction factors. Known runway occupancy times based on the actual runway/taxiway/exit system would consider all factors and would likely yield much longer runway occupancy times for use in the model, similar to the example in Table 4-3. Effects of Runway Crossings Aircraft taxiing across an active runway take time away from the runway’s primary purpose of accommodating arrivals and departures. Certain large natural gaps may occur in operations on a particular runway (e.g., because of required wake turbulence separations behind heavy jets or B-757 aircraft). Such gaps may permit one or more aircraft to cross the runway without affecting capacity. However, any gaps that do not permit such crossings will reduce the time available for the runway to be used for arrivals and departures. In addition, the use of multiple runway-crossing points is a common way for air traffic controllers to mitigate the adverse effects of runway crossings at airports that have significant numbers of runway crossings. Below is an approximate method for estimating the effects of runway crossings on the capacity of the runway being crossed. This method reflects the major runway-crossing parameters that affect hourly runway capacity: namely, the number of crossings per hour, the number of cross- ing points, the required runway-crossing clearance time, and the frequency of large natural wake turbulence gaps behind heavy jets and B-757 aircraft. The notes following Table 4-4 describe the steps to be taken in this method. Also shown in Table 4-4 are a number of sensitivity tests showing the effects of different assumptions regarding the number of crossing points, crossing clearance times, and percent of heavy jets or B-757s in the mix. The methodology described in Table 4-4 yields only a rough approximation. This methodology is most appropriately applied in the case of aircraft crossing a departures-only runway, which is typically the case at major airports with significant runway-crossing issues. The methodology can be adapted, however, to situations in which aircraft are crossing an arrivals-only runway or a mixed operations runway, both of which occur less frequently than aircraft crossing a departures-only run- way. Aircraft having to cross a mixed operations runway can be particularly difficult and disruptive. The Airfield Capacity Spreadsheet Model applies the same basic logic as outlined in Table 4-4 (see Figure 4-11). In particular, it includes an input section that asks the user to state whether Source: Landrum & Brown. Figure 4-10. Level 3 options for specifying type of parallel taxiway.

New airfield Capacity evaluation tools and Guidance 65 runway crossings occur or not. If runway crossings noticeably affect airfield capacity, the user inputs the average time (30 seconds, for example) the runway would be occupied to provide clearance for the crossing aircraft and the frequency of runway crossings in a design-hour. The model makes a general assumption that operations occur in the same manner as calculated, but runway availability is reduced from the maximum of 60 minutes. For example, 20 crossings per hour at 30 seconds per crossing reduces the maximum avail- able runway occupancy time for the operating aircraft fleet from 60 minutes to 50 minutes. This reduction would decrease the calculated runway capacity by nearly 17%. This same result would be obtained by inputting the same data in Table 4-4 and following Steps 1 through 8. Effects of an ATCT on Airfield Capacity An ATCT provides guidance for the movement of aircraft on and around an airport as they take off, land and taxi to or from the terminal area. The ATCT provides separation of aircraft on (1) Original Capacity Es�mate (2) Number of Crossings per Hour (3) Number of Crossing Points (4) Crossing Clearance Time (minutes) (5) Percent Heavy Jets / B-757s in Mix (6) Time Lost per Hour (minutes) (7) Reduced Capacity Es�mate (8) Percent Capacity Reduc�on 52 20 1 0.50 15.0% 8.5 44.6 14.2% 52 20 2 0.50 15.0% 4.3 48.3 7.1% 52 20 2 0.75 15.0% 6.4 46.5 10.6% 52 20 2 0.50 0.0% 5.0 47.7 8.3% 52 20 1 0.50 0.0% 10.0 43.3 16.7% (1) Es�mate the capacity of the runway without adverse effects of crossings (Column 1). (2) Es�mate the expected number of runway crossings during the peak hour from an analysis of runway use and aircra� taxiing pa�erns (Column 2). (3) Specify the number of crossing points. The effec�ve number of crossings then is calculated as the number of crossings per hour (Column 2) divided by the number of crossing points (Column 3). (4) Specify the es�mated or measured runway clearance �me (i.e., the �me between issuance of a runway- crossing clearance to the pilot and when the aircra� clears the other side of the runway). (5) Specify the percentage of heavy jets and B-757s in the aircra� fleet mix. (6) Es�mate the �me lost per hour as a result of the runway crossings, which is equal to (effec�ve number of runway crossings) x (clearance �me per crossing) x (1 − the percent of heavy jets/B-757s). (7) Mul�ply the original runway capacity by (60 minutes per hour − the �me lost per hour) divided by 60. (8) Compute the percent reduc�on in runway capacity by dividing (original runway capacity − reduced runway capacity) by original runway capacity. Source: LeighFisher. Table 4-4. Estimated reduction in capacity because of runway crossings (see steps in methodology below table). Yes 600 30 sec 20 Frequency Runway Crossing Delay ? Crossings during Peak Hour Average Crossing Delay Source: Landrum & Brown. Figure 4-11. Level 3 options for specifying runway crossings.

66 evaluating airfield Capacity the ground as well as in the airspace within 5 nautical miles of the airport. Most of the world’s airports are non-towered. Approximately 500 towered airports are in the United States (includ- ing both ATCTs operated by FAA and towers operated by private contractors). At airports with an ATCT, the methods and default assumptions discussed in this guidebook are appropriate for use in calculating airfield capacity. However, for airports without an ATCT, additional factors must be taken into account when determining capacity. The main determi- nants of capacity at a non-towered airport are weather conditions, equipage, and characteristics of the traffic and pilot population. A non-towered airport can be a challenging environment in which to evaluate airfield capacity because of the lack of ATC guidance and standard operating procedures. Although there are best practices for operating at non-towered airports, including special procedures for determining which runway to use, entering the traffic pattern, and announcing aircraft position and intent, very few legally mandated procedures exist. The precise effect of this lack of guidance and procedures on airfield capacity varies significantly from airport to airport, depending on the traffic level and level of sophistication of the aircraft and pilots operating at the airport. Moreover, little data are avail- able on how many aircraft can actually use non-towered airports. By contrast, at towered airports, controllers must keep daily records of the numbers of aircraft landing and taking off at the airport. The primary limitation on the capacity of a non-towered airport is the complex uncontrolled airport approach procedures for entering the traffic pattern, which require the pilot to overfly the airport to determine the appropriate runway use by observing the wind cone and number of aircraft in the pattern before maneuvering to enter the pattern as prescribed on the downwind leg. This maneuver is becoming more and more the exception, however, as more uncontrolled airports are being equipped with Automated Weather Observation Systems (AWOS) and Auto- mated Surface Observation Systems (ASOS). Also, arriving aircraft typically call other aircraft already operating at the airport to determine the active runway or are able to speak to an indi- vidual at a fixed-base operator (FBO) or other facility on the ground that is monitoring the common traffic advisory frequency (CTAF) or the airport’s individual frequency. In contrast, at a towered airport, controllers inform pilots which runways are in use and issue pattern-entry instructions. The ATCT controller is responsible for managing the number of aircraft in the traffic pattern and will instruct pilots to conduct full-stop landings if the traffic pattern becomes too full. In contrast, pilots operating at an uncontrolled airport have less ability to keep track of multiple aircraft in a flight pattern. At an airport without an ATCT, capacity can be higher than an airport with an ATCT, assuming that the right equipment is in place for pilots to communicate. However, if a CTAF is not in place at a non-towered airport, capacity could be significantly lower than at an airport with an ATCT. Touch-and-go operations generally remain within an airport’s traffic pattern, which is the stan- dard path followed by aircraft when taking off or landing while maintaining visual contact with the airfield and other aircraft in the pattern. In AC 90-66A, Recommended Standard Traffic Patterns and Practices for Aeronautical Operations at Airports without Operating Control Towers, FAA rec- ommends traffic patterns and operational procedures for various aircraft activities and identifies regulatory requirements for non-towered airports. An ATCT provides the greatest capacity benefit when the proportion of touch-and-go operations is relatively low (i.e., when most aircraft are entering and exiting the traffic pattern), because entering the traffic pattern is the most complex self-separation task and, therefore, tends to limit the capacity of uncontrolled airports. Effects of Radar on Airfield Capacity The availability of en route or airport surveillance radar can significantly affect airfield capac- ity. At airports where the air traffic controllers cannot use radar separations for arriving or

New airfield Capacity evaluation tools and Guidance 67 departing aircraft, procedural separation is used (e.g., the one-in, one-out rule or time-based separation requirements such as 10 minutes between successive arrivals). Procedural separations are many times larger than the minimum radar separation requirements. Airspace and air traffic rules governing the spacing of arrivals and departures and the use of multiple runways, which are critical determinants of airfield capacity, all depend on the avail- ability of radar for their execution. The ability of FAA to provide adequate separation between aircraft in the vicinity of an airport is dependent on the radar and communication capabilities of the system. Effects of Staggered Runway Thresholds Lateral separations between centerlines of parallel runways determine the relationships and ATC procedures required between the runways. At airports where the ends of two parallel run- ways are staggered, or offset from one another, the assumed centerline-to-centerline separation of the runways must be adjusted. Simultaneous arrivals to one runway and departures from a parallel runway require at least a 2,500-foot separation between runway centerlines. For simul- taneous arrival/departure operations on staggered parallel runways, the required lateral separa- tion of the runways depends on the magnitude and direction of the stagger. Staggered runways fall into two categories: (1) favorable stagger or (2) adverse stagger. For additional information, please refer to Chapter 3, Figure 3-25, of FAA AC 150/5300-13A, Airport Design. Favorable Stagger When the runway thresholds are staggered such that the approach is to the near threshold, the requirement for 2,500 feet centerline-to-centerline separations between runways can be reduced by 100 feet for each 500 feet of threshold stagger, down to a minimum separation of 1,000 feet (or 1,200 feet for Airplane Design Group V or VI runways). This situation is shown in Figure 4-12. Adverse Stagger When the runway thresholds are staggered such that the approach is to the far threshold, the requirement for 2,500 feet centerline-to-centerline separation between runways must be increased by 100 feet for each 500 feet of threshold stagger. This scenario is shown in Figure 4-13. When these minimum separation requirements between the parallel runway centerlines are satisfied, arrivals and departures can be assumed to be independent in IMC; otherwise, arrivals and departures should be assumed to be dependent. Source: Presenta�on & Design Inc., based on FAA standards. Figure 4-12. Favorable stagger showing reduction in required separation of 100 feet for every 500 feet of stagger.

68 evaluating airfield Capacity Effects of Aircraft-Specific Runway Use Restrictions At many airports, and for a variety of reasons, the same aircraft fleet mix does not use all runways. When only small differences exist in the fleet mix using the different runways, the variations do not significantly affect overall airfield capacity. At some airports, however, large differences exist in the fleet mix using the different runways, typically because of noise abatement considerations or runway length. For example, certain runways cannot be used by jet aircraft because of adopted noise abatement flight procedures. Moreover, some commercial airports may designate a runway to be used exclusively by small general aviation aircraft. At airports with these types of runway use limitations, the overall airfield capacity has to be estimated by analyzing the capacities of the individual runways and then deriving the overall capacity by enforcing aircraft fleet mix proportions (i.e., recognizing that certain runways may be underutilized because of the limitations on the aircraft types they can accommodate). Under such circumstances, it is particularly important to realize that the capacities of individual run- ways are not additive. For example, if a runway can accommodate only about 10% of the aircraft types operating at an airport, it will not contribute as much to airfield capacity as a runway that can accommodate any aircraft type. If a runway is limited in the types of aircraft it can accommodate, the fact that its full capacity may not be usable must be taken into account, and adjustments must be made to reflect these restrictions in the estimate of the airport’s overall airfield capacity. Only Level 4 and Level 5 mod- els can explicitly account for such restrictions when estimating capacity. When using lower levels of modeling sophistication, manual adjustments must be made to account for the variations in aircraft fleet mix when aircraft classes are separated by runway. The goal of these manual adjust- ments is to make the calculations conform to the actual mix of aircraft using certain runways, so that a runway that is restricted to certain types of aircraft only contributes to capacity to the degree that aircraft in the mix can use the runway. Consider two independent parallel runways, one restricted to Class A aircraft, and the other used by Class B, Class C, and Class D aircraft. To estimate the capacity of this airfield system, the capacity of each runway would be estimated, and then a manual adjustment would be made to enforce the mix. The first step would be to readjust, or normalize, the airfield mix to reflect the types of aircraft using each runway. Table 4-5 presents an example of runway-specific fleet mixes. Source: Presenta�on & Design Inc., based on FAA standards. Figure 4-13. Adverse stagger showing increase in required separation of 100 feet for every 500 feet of stagger.

New airfield Capacity evaluation tools and Guidance 69 Following the estimation of fleet mix by runway, capacity is estimated for each runway using that runway’s fleet mix. The estimated capacities of Runways 1 and 2 in this example are shown in Table 4-6. To calculate the appropriate overall airfield capacity for this example, the airfield fleet mix can be enforced by applying the following formula: Capacity minimum=     c p c pA B C D 1 2, , , Where: c1 = Capacity of Runway 1 (Class A only) pA = Proportion of airfield mix that is made up of Class A aircraft c2 = Capacity of Runway 2 (Classes B, C, and D only) pB,C,D = Proportion of airfield mix that is made up of aircraft in Classes B, C, and D In the example described above, the overall hourly airfield capacity would be calculated as follows: Airfield capacity minimum=     78 0 18 60 0 82. , .  = 73operations This calculation reflects the fact that Runway 1 would be underused, as only 18% of the aircraft mix is eligible to use the runway, while Runway 2 would be operating at full capacity. Moreover, Runway 1 would contribute only 13 operations (or 18%) to the total hourly airfield capacity. Estimating Effects of NextGen on Airfield Capacity The implementation of Next Generation Air Transportation System (NextGen) technologies and capabilities is expected to enable increases in airfield capacity through a variety of opera- tional improvements. Many of these operational improvements are related to enabling multiple Percent in Each Aircra� Class TotalA B C D Airfield mix 18% 3% 76% 3% 100% Runway 1 mix (Class A) 100% 0% 0% 0% 100% Runway 2 mix (Classes B, C, D) 0% 4% 92% 4% 100% Source: LeighFisher. Table 4-5. Runway-specific fleet mix adjustment. Runway Hourly Runway Capacity(50% arrivals) 1 78 2 60 Source: LeighFisher. Table 4-6. Estimated hourly capacity by runway.

70 evaluating airfield Capacity required navigational performance (RNP) approach procedures where multiple instrument landing system (ILS) approaches cannot be conducted today. In particular, these RNP approach procedures are expected to have the following benefits: • Potential for more simultaneous movements and, therefore, greater capacity • Reduced pilot and controller workload • Reduced aircraft separations and obstacle-clearance standards • More efficient horizontal and vertical profiles resulting in reduced fuel consumption and emissions One of the core technologies of NextGen is the GPS-based Automatic Dependent Surveillance- Broadcast system being implemented today in the United States. This surveillance system is expected to ultimately supplement or replace existing legacy radar systems in the en route and terminal area airspace, because it will display aircraft position more accurately and enable a reduction in the achievable average separations between aircraft. NextGen technologies and operational improvements will be gradually phased in as more airlines equip their aircraft and more enabling ATC rule changes and flight procedures are imple- mented. The precise timing and benefits of these NextGen operational improvements are not well defined, but they are expected to be implemented over time, with the most significant ben- efits occurring in the long term. Nevertheless, FAA and the NextGen Joint Planning and Development Office have sponsored many airfield and airspace capacity studies aimed at estimating the capacity benefits of NextGen. In most of these studies, specific assumptions have been developed for several of the parameters commonly used in airfield capacity and simulation models. For example, the NextGen operational improvements mentioned above are typically repre- sented by (1) a reduction in the statistical spacing buffer used in these models; (2) a reduction in the minimum required aircraft separations, particularly in IMC; and (3) the ability to conduct more simultaneous movements on a given set of runways. Assumptions regarding statistical spacing buffers have long been used to estimate the benefits of future ATC technologies. In analyzing airfield capacity, the spacing buffer generally is assumed to be represented by a standard deviation of 18 seconds in the ability of controllers to deliver aircraft to the final approach. In recent analyses conducted for the NextGen Joint Planning and Development Office, this spacing buffer was assumed to be reduced to about 12 seconds in the near term, and to about 6 seconds in the far term, where near term and far term are not pre- cisely defined. The update of the FAA’s Airport Capacity Benchmark Report for 2012 is expected to assume a reduced spacing buffer of 16.5 seconds for estimating airfield capacities for certain runway ends to represent recent implementation of the Traffic Management Advisor. The Traffic Management Advisor is a tool used in many FAA Air Route Traffic Control Centers (ARTCCs) and terminal radar approach controls (TRACONs) to sequence and schedule aircraft move- ments. This reduction in separation can only be applied to certain runway ends. In certain other analyses performed for the NextGen Joint Planning and Development Office, analysts have assumed a reduction in IMC separation requirements of 1 nautical mile, reductions in AROTs and DROTs of 5 to 10 seconds, and independent approaches to closely spaced parallel runways. As of yet, no official, agreed-upon set of operational improvements and assumptions exists for use in estimating the airfield capacity benefits of NextGen—and they may never be, because estimating the benefits of NextGen will remain an airport-specific challenge, depending on aircraft equipage, runway configuration, demand characteristics, and other factors. For the foreseeable future, it is expected that coordination with FAA will be needed to agree on relevant assumptions about NextGen capacity benefits.