Evaluating Airfield Capacity (2012)

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B-1 B-1 APPENDIX B ESSENTIAL REFERENCES AND DATA SOURCES Appendix B includes a list of essential references for airfield capacity analysis and a list of sources of information about each of the 21 c apacity f actors . ESSENTIAL REFERENCES This list of essential references for airfield capacity analysis includ es FAA documents, airfield capacity documents, FAA Air Traffic Control orders and manuals, in ternational airfield capacity documents, future technologies and systems, data sources, models, government and university documents and papers. 1. FAA Airfield Capacity Documents 1.1 Federal Aviation Administration: Office of Airport Planning & Programmin g, Planning & Environmental Division (1983) . Advisory Circular 150/5060 - 5 , Airport Capacity and Delay [PDF d ocument] . Retrieved from http://www.faa.gov/doc umentLibrary/media/advisory_circular/150 - 5060 - 5/150_5060_5.pdf . This document contains instructions and steps to compute the airport capacity and aircraft delay for airport planning and design purposes . This circular is primarily intended for airport planners. The throughput method is used to calculate the airport capacity and aircraft delay. These calculations can be used over 19 runway use configurations with m ultiple arrival streams restricted to para llel runway configurations. For most calculations, the airport is assumed to have at least one ILS - equipped runway , in addition to air traffic control facilities and services to conduct operations even in a radar environment . “ Missed approach protection is assured for all converging operations in IFR weather conditions , ” which are assumed to occur 10% of the time. It is assumed that for 80 % of the time , an airport is operated with the runway - use configuration w hich produces the greatest hourl y capacity . Cha pter 5 identifies (then available) co mputer models for runway capacity and aircraft delay analyses , which include SIMMOD, ADSIM , and the FAA Airfield Capacity Model (ACM) . 1.2 Federal Aviation Administration & The MITRE Corporation (2007) . Capacity Needs i n the National Airspace System , 2007 – 2025 [PDF document]. Retrieved from http://www.faa.gov/about/initiatives/nextgen/defined/why/cap%20needs%20in%20t he%20NA S.pdf . This report is “ an assessment of the future capacity of the Nation’ s airports and metropolitan areas ,” trying to identify the locations with the highest need for increased capacity (the airports in the metropolitan region.) This report is intended for air traffic management personnel. Future operations forecasts were generated using the Terminal Area For ecast (TAF) version 2005 , prepared by the FAA Office of Aviation Policy and Plans (APO) , and Future Air Traffic Timetable Estimator (FATE) 1 , a socio - economic model developed by MITRE CAASD . TAF predicts future operations on an airport - by - airport basis. T he Enhanced Airfield Capacity Model (E - ACM) was also used to calculate “ the average number of arrivals and departures that can be expected during busy periods at an airport based on air traffic control (ATC) procedures, including separation minima, and the p robabilistic characteristics of aircraft performance .” This analysis factored improvements affecting runway capacity such as new/extended runways, new/revised control procedures, and redesigned airspaces, most of which were available from 1 Bhadra, D., J. Gentry, H. Brendan, & M. Wells (2005) . Future Air Traffic Timetable Estimator . Journal of Aircraft , 42(2) , 320 – 8.

B-2 Evaluating Airfield Capacity B-2 Operational Evolu tion Plans. The existing environmental restrictions impact ing runway capacity, such as noise abatement procedures, were assumed to exist for the duration of the forecast. 1.3 U.S. Department of Transportation, Federal Aviation Administration, & The MITRE C orporation (2004) . Airport Capacity Benchmark Report 2004 [PDF document]. This report provides benchmark values for capacity, defined as “ the maximum number of flights an airport can routinely handle in an hour, for the most commonly used runway configurat ion in each specified weather condition,” at the 35 airports listed in the FAA’s Operational Evolution Plan (OEP) version 5.0 . For each airport, capacity for three different weather scenarios (Optim al or VFR conditions; I FR conditions; and Marginal , which is halfway between VFR and IFR conditions ) is calculated . “ These benchmarks are estimates of a complex quantity that varies widely with weather, runway configuration, and the mix of aircraft types. Capacity benchmarks assume there are no constraints in the en route system or the airport terminal area .” The benchmark values are the sum of (maximum) number of takeoffs and landings per hour t hat are possible under the conditions, if the demand is present . The frequency of the three weather conditions at each airport was determined using the FAA Aviation System Performance Metric ( ASPM ) database data. This analysis factored technical and procedural improvements affecting runway capacity such as Simultaneous Offset Instrument Approaches (SOIA), Standard Te rminal Automation Replacement Systems (STARS), Traffic Management Advisor (TMA) and Area Navigation (RNAV), most of which were available from Operational Evolution Plans. This report does not, however, explicitly mention the methodology or model used for o peration forecasts. This report is intended for air traffic management personnel. 1.4 The MITRE Corporation (1978). Parameters of Future ATC Systems Relating to Airport Capacity/Delay (Report No. FAA - EM - 78 - 8A) . Washington, DC: Andrew Haines. This report p resented a model to quantify the impact of Wake Vortex Advisory/Avoidance Systems (VAS/WVAS) and the Terminal Area Metering and Spacing (M&S) tools on the spacing on final approach. Of particular interest are tables 3 - 2 and 3 - 3. They provide (then) observe d and predicted minimum separation under saturated VFR conditions for arrivals and departures. However, these minimum separation values continue to be current. The aircraft classes defined in the model (small, heavy, large) continue to be current. 2. FAA A irspace and Air Traffic Control (ATC) Orders and Manuals 2.1 U.S. Department of Transportation & Federal Aviation Administration (2010) . Order JO 7210.3 X Facility Operation and Administration [PDF document]. Retrieved from http://www.faa.gov/documentLibrary/media/Order/FAC.pdf . “ This order provides instructions, standards, and guidance for operating and managing air traffic facilities .” Of particular interest are sections 6 - 7 (User Request Evaluation Tool URET), 8 - 2 (NAS En Route Automation Procedures), 10 - 7 (Airport Arrival Rate AAR), 17 - 8 (Monitor Alert Parameter), 17 - 9 (Ground Delay Program), 17 - 10 (Ground Stop [ s ] ), and 17 - 14 (Severe Weather Avoidance Plan SWAP) . 2.2 U.S. Departme nt of Transportation & Federal Aviation Administration (2010). Order JO 7110.65 U Air Traffic Control [PDF document]. Retrieved from http://www.faa.gov/documentLibrary/media/Order/ATC.pd f . “ This order prescribes air traffic control procedures and phraseology for use by persons providing air traffic control services. ” It covers procedures for various types of flights, flight plan information (its needs and type of information), flight stri ps, communications, signals , and reporting information. Of particular interest are sections 2 - 1 - 4 (Operational Priority), 2 - 1 - 19 (Wake Turbulence), 2 - 1 - 27 (TCAS

Appendix B B-3 B - 3 Resolution Advisories), 2 - 1 - 28 (RVSM Operations), 2 - 8 - 2 (Arrival/Departure Runway Visibility), 3 - 1 - 6 (Traffic Information), 3 - 1 - 7 (Position Determination), 3 - 1 - 11 (Surface Area Restrictions), 3 - 3 (Airport Conditions), 3 - 8 (Spacing and Sequencing), 3 - 9 (Departure Procedures and Separation), 3 - 10 (Arrival Procedures and Separation), 4 - 8 (IFR Approach Clearance Procedures), 5 - 5 (Radar Separation), 6 - 2 (Non - Radar Initial Separation of Successive Departing Aircraft), 6 - 3 (Non - Radar Initial Separation of Departing and Arriving Aircraft), 6 - 4 (Longitudinal Separation), 6 - 5 (Lateral Separation), 6 - 6 (Vertica l Separation), and 7 - 2 (Visual Separation.) This order is intended for air traffic control personnel and pilots. 2.3 U.S. Department of Transportation & Federal Aviation Administration (1993) . Order JO 7110.98A Simultaneous Converging Instrument Approaches (SCIA) [PDF document]. Retrieved from http://www.faa.gov/documentLibrary/media/Order/ND/7110.98A.pdf . This order defines the operational criteria and authorized procedures for conducting simultaneous instrument approaches on converging runways. SCIA procedure is permitted under operating conditions which include operational control tower, o perational radar , ILS on each runway, and non - intersecting final approach courses. This pr ocedure permits straight - in approaches only. Converging approaches cannot be conducted simultaneously on intersecting runways for decision heights lower than 1,000 ft or visibility less than 3 nm. This order is for aiding air traffic control personnel. 2.4 U.S. Department of Transportation & Federal Aviation Administration (1995). Order 7110.110A Dependent Converging Instrument Approaches (DCIA) With Converging Runway Display Aid [PDF document] . Retrieved from http://www.faa.gov/documentLibrary/media/Order/7110.110A.pdf . SCIA procedures are sometimes not applicable for decision heights lower than 1,000 feet . To address this concern, incre asing “ airport capacity in IFR weather conditions for airports with converging intersecting or nonintersecting runways ,” this order identifies the DCIA procedure . This procedure makes use of staggered approaches and accounts for m issed approaches by aircra ft on two convergi ng approaches occurring within two minutes . DCIA procedure is permitted under operating conditions which include operational control tower, o perational radar and ARTS , o perational CRDA tool, ILS / MLS on each runway, operational navigatio nal aids, and an open communications link between pilot and controller. The final approach courses need to be non - intersecting with the i ncluded angle between the runway approach courses between 45 degrees and 1 20 degrees . Appendix 1 provides the stagger d istances and restrictions for various decision heights, runways configurations , and distance to intersection. This order is enacted for aiding air traffic control personnel. 2.5 U.S. Department of Transportation & Federal Aviation Administration (2000) . Or der JO 7110.118 Land and Hold Short Operations [PDF document]. Retrieved from http://www.faa.gov/documentLibrary/media/Order/7110.118.pdf Previous defined simultaneous operations o n intersecting runways (SOIR) included landing and holding short of an intersecting runway. This order (LAHSO) expands on SOIR to include holding short of taxiway, holding short of approach/departure flight path and holding short of predetermined points on the runway (other than on a runway or taxiway) . For commercial flights, LAHSO operations are permitted under certain conditions , which include 5 miles or greater visibility (unless PAPI/VASI - equipped runway), 1,500 ft decision height, and on a runway with electronic/visual glide scope indicator. Appendix 1 provides a table for identifying aircraft that can successfully conduct LAHSO procedure based on the available landing distance. This order is for aiding air traffic control personnel.

B-4 Evaluating Airfield Capacity B-4 2.6 U.S. Department of Transportation & Federal Aviation Administration (2008) . Order JO 7110.308 CHG 1.5 NM Dependent Approaches to Parallel Runways Spaced Less Than 2500 f t Apart [PDF document]. Retrieved f rom http://www.faa.gov/documentLibrary/media/Order/JO7110.308CHG%201.pdf . “ This type of dependent instrument approach can be conducted for airports with specific centerlin e separations and threshold staggers. The lead aircraft of the dependent pair is restricted to being small or large aircraft weight type and is cleared to the lower approach. The geometry of the approach, with small or large aircraft leading on the lower a pproach, as well as the lateral separation between the two approaches, provide wake turbulence avoidance necessary for this reduced separation dependent approach operation. Lateral separation between the two approaches contributes to wake avoidance. In add ition, a small glide path height difference may be necessary, especially at distances of 7 or more nautical miles from touchdown, to ensure the trailing aircraft is at or above the height of the leading aircraft in the reduced separation pair. The required glide path height can be achieved through displaced landing thresholds or through small glide path angle differences that are permitted within the constraints of precision approaches. ” Table TBL - A - 1 in Appendix 1 identifies the runway pairings where thes e approaches can be conducted and lists various glide path values for approaches at these runways. This order is for aiding air traffic management and air traffic control personnel. 2.7 U.S. Department of Transportation & Federal Aviation Administration (2 008) . Order N JO 7110.478 Interim Procedures for A380 Proving and Promotional Flights [PDF document]. Retrieved from http://www.faa.gov/documentLibrary/media/Notice/ND/N%20JO%207110.478.pdf . This cancelled order had defined the in - trail separation applicable for the Airbus A380 aircraft to account for possible larger wake vortices. An aircraft trailing the A380 en-route was required a minimum of 5 nm separation. A minimum of 10 nm spacing was required when transitioning to terminal airspace. Within terminal airspace, separation of 6 nm (for heavy aircraft behind A380), 8 nm (for large aircraft behind A380) and 10 nm (f or small aircraft behind 380) were required. Operations on parallel runways less than 2,500 ft apart were to be reduced to single runway operations to account for wake turbulence. This order wa s for aiding air traffic control personnel. This order has not been superseded by another order. 2.8 Federal Aviation Administration (2010). Aeronautical Information Manual: Official Guide to Basic Flight Information and ATC Procedures . Retrieved from http://www.faa.gov/air_traffic/publications/ATpubs/AIM/ “ This manual is designed to provide the aviation community with basic flight information and ATC procedures for use in the National Airspace System (NAS) of the United States .” “ It also contains items of interest to pilots concerning health and medical facts, factors affecting flight safety, a pilot/controller glossary of terms used in the ATC System, and information on safety, accident, and hazard reporting. ” Of particular interest here are secti ons 3 - 1 (General) , which highlights VFR minimums ; 3 - 2 (Controlled Airspace) , which highlights the operating rules for various airspaces ; 4 - 4 (ATC Clearances and Aircraft Separation) , which identifies role of pilot for implementing separation, 5 - 2 (Departur e Procedures) ; 5 - 3 (En Route Procedures) ; 5 - 4 (Arrival Procedures) ; and 5 - 5 (Pilot/Controller Roles and Responsibilities) . Th e primary audience for this manual is aviators.

Appendix B B-5 B - 5 3. International Airfield Capacity Documents 3.1 National Air Traffic Services , Ltd. (2003). A Guide to Runway Capacity for ATC, Aircraft and Airport Operators . West Drayton, Middlesex UK: Richard Everitt. This guide is intended for air traffic controllers, airport personnel , and aircraft operators. It explains (then) current issues affect ing runway capacity at the UK’s major airports , the process of assessing a runway’s capacity operates , and includes and an overview of (then) planned developments to maximi ze runway capacity . It explains, using layman ’s terms and analogies, the key f actors affecting runway capacity , such as runway configuration, wake vortex separation, and aircraft sequencing . UK separation standards for approach (3nm radar separation; 2.5 nm final approach separation) are available in this section. The process to det ermine runway capacity is also described at a very high level. This includes simulation of delays under full capacity using actual data on arrivals/departures, aircraft , and time - on - ground. (Then) f uture technologies identified in the guide were time based (4D) separation on final approach, precision landing aids, wake vortex detection systems, spacing tools, departure metering tools, CAT III MLS system, Advanced Surface Movement Guidance and Control Systems, and Runway Incursion Monitoring & Conflict Aler ting Systems. 4. Future Technologies and Systems 4.1 Joint Planning and Development Office (2010). Joint Planning and Development Office : Making NextGen a Reality. Retrieved from http://www.jpdo.gov/ . 4.2 Federal Aviation Administration (2008). Operational Evolution Partnership. Retrieved from http://www.jpdo.gov/library/20070726AllHands/20070727_JPDOA llHandsMeeting_OEP_Sypnie wski_FINAL.pdf . 4.3 Federal Aviation Administration: NextGen Integration and Implementation Office (2009) . FAA’s NextGen Implementation Plan 2009 [PDF d ocument]. Initially r etrieved from http://www.faa.gov/about/initiatives/nextgen/media/ngip.pdf . This document is updated annually. At time of publication, the current document was available from http://www.faa.gov/nextgen/implementation/media/NextGen_Implemen tation_Plan_2012.pdf. 4.4 Joint Planning and Development Office (2009). Concept of Operations for the Next Generation Air Transportation System version 3.0 [PDF d ocument]. Retrieved from http://www.jpdo.gov/library/NextGen_ConOps_v3%200.pdf . 5. Data Sources 5.1 Federal Aviation Administration (2010). FAA Aviation System Performance Metrics (ASPM). Available at http://aspm.faa.gov/aspm/entryASPM.asp . The Aviation System Performance Metrics (ASPM) provides data on IFR flights to and/or from major airports (approx 77); and all flights by specified carriers (appr ox 22). Flights include those involving international and domestic airports. The ASPM data includes: airport weather (VMC, MIMC, IMC) , runway configuration , declared arrival rates , and dec l ared departure rates . Online access to the ASPM system requires a registered user name and password , which may be requested from FAA by completing an Access Request at https://aspm.faa.gov/main/sysMailTo.asp?area=aspm .

B-6 Evaluating Airfield Capacity B-6 5.2 Federal Aviation Administration (2010). FAA Airline Service Quality Performance System (ASQP). Available at https://aspm.faa.gov/asqp/sys/ . The Airline Service Quality Perfor mance System (ASQP) includes data provided by the airlines on a flight - by - flight basis from airlines that carry at least 1% of all domestic passengers. The number of airlines that meet this criteri on has varied from 10 to 20 over the past two decades . Actu al and scheduled time is available for gate departure and gate arrival. The airlines also provide the actual wheels - off time so that taxi - out time can be computed and wheels - on time so that taxi - in time can be computed. In addition, the airlines provide ca usal data for all flights arriving 15 minutes past their scheduled arrival time. The data is available from June 2003 and is updated on a monthly basis. The causes of delay categories are Airline, Extreme Weather, National Aviation System, Security, and la te arriving flight. Online access to the ASQP system requires a registered user name and password , which may be requested from FAA by completing an Access Request at https://aspm.faa.gov/main/sysMailTo.asp?area=asqp . 5.3 Federal Aviation Administration (2010). FAA Operations Network (OPSNET). Available at https://aspm.faa.gov/opsnet/sys/ . Operations Network (OPSNET) provides data on air traffic operations and delay da ta. All FAA ATC facilities (with the exception of flight service stations [ FSS ] ) record OPSNET data , which is provided to the FAA ATO System Operations, Quality Assurance (QA) on a daily basis. The ATCSCC QA then processes the data and stores them into the OPSNET database. OPSNET records the following information and data: Airport Operations (IFR itinerant and VFR itinerant operations [ arrivals and departures ] , local operations at the airport as reported by a ir t raffic c ontrol t owers [ ATCTs ] ; t ower o perati ons (IFR and VFR itinerant operations [ arrivals and departures ] , IFR and VFR overflights, and local operations worked by the tower) ; TRACON o perations (IFR and VFR itinerant operations and overflights worked by the TRACON) ; t otal t erminal o perations (total operations worked by any facility based on the functions at the facility) ; ARTCC o perations (domestic and oceanic departures and overflights and total aircraft handled) ; f acility i nformation ( facility name and type, region, state, and hours of oper ation for each air traffic control facility) ; and d elays (reportable delays). To access OPSNET data, users require a login. A user name and password may be requested from FAA by completing an Access Request at https://aspm.faa.gov/main/sysMailTo.asp?area= opsnet . Without a login, users can access the official count released to the public from Air Traffic Activity System (ATADS) , w hich provides data after the 2 0th of th e month for the previous month. 5.4 Federal Aviation Administration (2010). FAA Air Traffic Activity Data System (ATADS). Available at https://aspm.faa.gov/opsnet/sys/Main.asp?force=atads . ATADS data is OPSNET data that is made available after the 20 th of the month for the previous month. See discussion OPSNET above. Access to ATADS data does not require a registered user name and password. 5.5 Federal Aviation Administration (2010). FAA Terminal Aerodroms/Airport Forecast (TAF s ). Available at http://weather.noaa.gov/weather/taf.shtml . The Terminal Aerodrome/Airport Forecast (TAF) provides weather forecast information. TAFs use similar encoding to METAR reports ( message d’observation météorol ogique pour l’aviation régulière / meteorological observation message for routine aviation). TAFs apply to a five statute mile radius from the center of the airport runway complex. Generally, TAFs can apply to a 9 - or 12 - hour forecast; some TAFs cover an 18 - or 24 - hour period. TAFs for some major airports cover 30 - hour periods.

Appendix B B-7 B-7 5.6 Research and Innovative Technology Administration (RITA), Bureau of Transportation Statistics (2011). Airline and Airport Information . Available at http://www.bts.gov/programs/airline_information/ . The Bureau of Transportation Statistics (BTS) provides data and statistics on the performance and finances of the air transportation system. This information is derived from FAA data , ASPM, ASQP, and OPSNET, as well as Form 41 data provided by airlines on a monthly/quarterly basis. BTS Airline and Airport data and statistics include: a ir t raffic ( a ir t raffic h ubs) ; a ircraft: t ypes, a cquisition d ates and o perating s tatus ( a vailable s eat - m iles, b aggage f ees, d istance between a irports) ; e conomic r esearch ( e mployment) ; f ares ( d omestic a irfares reported by r oute) ; f inancial s tatistics ( f lights , f reight , f uel c ost and c onsumption , and l oad f actor ) ; o n - t ime p erformance ; o perating p rofit/ l oss ; o perating r evenue ; p assengers ; p assengers d enied c onfirmed s pace r eport ; n ew r eservation c ancellation/ c hange f ees by a irline ; r evenue p assenger - m iles ; and t armac t imes . 5.7 Federal Aviation Administration (2010). Performance Data Analysis and Reporting (PDARS). Available on request from FAA . PDARS data and measurements are based on the processing of radar track and flightplan data collected from Automatic Radar Terminal System (ARTS) computers at the TRACONs, and data collected fro m the h ost computers at the ARTCCs. This data provides mo r e details of tracks flown than the Enhanced Traffic Management System (ETMS) or its commercial ASD Feed (used in the industry). The type s of information generated from PDARS data include: t ravel ti mes within geometric ar eas (sectors, facilities, etc.) ; t ravel times for routing segments (arrival fix to runway, runway to departure fix, facility boundary to/from runway, etc.) ; f low counts over user - defined points ; t hroughput co unts for airports, sector s, etc .; g roundspeed distributions at user - defined times and areas ; and i dentification of aircraft deviating from a prescribed procedure. 5.8 Federal Aviation Administration (2010). Airport Surface Detection Equipment, Model X (ASDE - X). Available on request from FAA . ASDE - X data provides track and identification data for aircraft and equipped vehicles on the airports surface as well as aircraft within approximately 5 miles of the airport. The core ASDE - X track data includes a radar sen sor and a multilateration sensor capable of receiving Automatic Dependent Surveillance — Broadcast (ADS - B) from the aircraft. The ASDE - X Multiprocessor subsystem combines independent surveillance data from the r adar subsystem with cooperative and dependent d ata from the m ultilateration subsystem to provide a single target with Flight ID for d isplay to a ir t raffic c ontrol. The m ultiprocessor subsystem may also integrate data from Automated Radar Terminal System (ARTS) and the Standard Terminal Automation Repla cement System (STARS) as well as the Airport Surveillance Radar (ASR ) , dependent on the implementation chosen for a given site . In 2010, 35 major airports had ASDE - X equipment installed. The type s of information generated from ASDE - X surface trajectory dat a include: travel times within geometric ar eas (e . g . , ramp, taxiway, runway) ; hold times (e.g., gate, ramp spot, departure runway queue) ; f low counts at specified locations (e.g., ramp spot, runway threshold) ; th roughput co unts for gates, runways, taxiways and airports ; groundspeed distributions on approach, departure, runways, taxiways and ramps ; and i dentification of aircraft deviating from a prescribed procedure.

B-8 Evaluating Airfield Capacity B-8 6. Models 6.1 LMI Government Consul�ng (2009). Catalog of Models for Assessing the Next-Genera�on Air Transporta�on System (Report NS802T2). Washington, DC: Dou Long, Shahab Hasan, Antonio Trani, & Alan McDonald. This report reviews a comprehensive list of avia�on models. The models are separated into two broad categories—airport and airspace—and minor categories: runway, airfield, terminal, and network of airports. The authors note the strength of the runway models is “their ability to explore a mul�tude of airport scenarios in a short amount of �me. By design, these models lack airfield and airspace network representa�on in order to keep the input requirements to a minimum. The runway models make simplifying assump�ons about runway opera�onal procedures. The vast majority of them rely on a separa�on matrix approach to es�mate the capacity of a runway configura�on for a given set of airport condi�ons (weather, available ATC technology, etc.).” The authors believe these models suffer from the poten�al inability to accurately predict airfield capacity influenced by other airport airside components. The models, representing airfield and aircra� state variables and processes, were found to be more realis�c because of their integral network connec�vity. The authors note that it is unclear “if the current models would be able to model complex and adap�ve air traffic flow management procedures for ground opera�ons that are affected by far-away airports or ground-hold decisions due to airspace restric�ons. Some of the tools reviewed had scrip�ng capabili�es that provide room to reassign aircra� to runway queues based on dynamic traffic condi�ons. But the elements to handle op�miza�on tasks related to ground holds and system-wide ground path op�miza�on driven by far-away events expected in NextGen seem difficult to model. Some models support unique procedures such as deicing rules and limited op�miza�on of paths on the ground.” The aircra� behavior in different network type models varied. Most models seemed to have adopted a simplis�c approach to model aircra� behavior using an implicit specifica�on of the aircra� performance driven by waypoint flight trajectories with �me tags, observed routes, and speeds. The authors also note that “the correla�on between simula�on framework complexity and aircra� detailed specifica�on in every model is very evident. The run�mes for these models vary significantly as the complexity of the aircra� and ATC model increases. Some of the models can execute a NAS-wide run in a few minutes. Others require many hours (or even days) to run a scenario with thousands of flights.” The discrete-event, agent-based, simula�on models for terminal (or STARS in) were found to provide a be�er level of fidelity of flights. These models were scalable to model single airports or hundreds of airports, including details of the terminal airspace. The authors noted that the run�mes could vary substan�ally among models. For example, it is not uncommon to find that some of the models take 10 to 20 interac�ons to execute the same airspace scenario if the conflict resolu�on procedures are turned on in the model. 6.2 Massachuse�s Ins�tute of Technology: Interna�onal Center for Air Transporta�on (1997). Exis�ng and Required Modeling Capabili�es for Evalua�ng ATM Systems and Concepts. Cambridge, MA: A.R. Odoni, J. Bowman, D. Delahaye, J.J. Deyst, E. Feron, R.J. Hansman, K. Khan, J.K. Kuchar, N. Pujet, & R.W. Simpson. This report assessed the strengths and weaknesses of (then) exis�ng fast-�me models and tools for the study of ATM systems and concepts and helped iden�fy and priori�ze the requirements for the

Appendix B B-9 B - 9 development of additional modeling capabilities in the (then) near future. The models reviewed in this report have nine categories. The authors found that c apacity and delay models, as a group, represent ed the most advanced and accur ate models. They note , however, that these models have four main problems: lack of familiarity, extensive resources required to run these models, lack of adaptability to model future operations (since improved) , and lack of environmental validity. For con flict detection and resolution models, the authors found that the models typically used specified flight plans or randomly generated flight plans (based on desired traffic density.) The aircraft dynamics in the model were simple and generic. Conflict dete ction models were rule - based on simple geometrical criteria, such as miss distance or penetration of safety buffers around each aircraft . Only a couple of models were found to include the capability to model trajectory uncertainty. For models of human fact ors and human/automation modeling, the authors note that it is important to consider the validity and flexibility of a model vis - à - vis different applications. “ A microscopic model like MIDAS provides a very detailed description of how the human operates an d can provide insight into where bottlenecks are and how performance could be improved . However, whether the additional level of detail in MIDAS is really needed and whether a given version of MIDAS can be extended to cover a novel situation still needs to be determined. ” The authors identify this area as needing great attention in terms of advanced model development, modeling system safety with human/automation, model validations, and the integration of these models with other models. Only two cost/benefit analyses were (then) found . ACIM was found to be an effective and mature tool for projecting growth and demand in both the airline and commercial aircraft industries. 6.3 ACATS 6.3.1 Barrer, J.N., P. Kuzminski, & W.J. Swedish (2005) . Analyzing the Runway Capacity of Complex Airports . Proceedings of the 5th Aviation, Technology, Integration, and Operations Conference , Arlington , VA, pp. 1 – 7. This paper includes descri ptions of the Airport Capacity Analysis Through Simulation (ACATS) model developed by The MITRE Corporation . This paper d escribe s the graphical user interface, the simulation module, and the methodology behind the algorithms of ACATS. “ The user interface for ACATS provides a fast way to set up the elements of the airport that are essential for calculating runway capacity. It also supports the use of Air Traffic Control (ATC) separation rules that may become feasible as technology improves. The software in the user interface automatically converts the data for any airport into a standardized set of files that are then processed by the ACATS simulation software. ” “ The output of ACATS includes an animation of the simulation, statistics about the observed throughput, and a set of graphical analysis charts. The animation and graphical results produced by ACATS are important tools in explaining the analysis to the end user and in validating the results of the simulation. ” 6.3.2 Barrer, J.N. (2006) . Airport Capacity Through Simulation (ACTS) Transition [PDF PowerPoint slides] . Retrieved from http://www.mitre.org/news/events/tech06/briefings/1493.pdf . This brief presentation highlights the need for a capacity analysis tool and provides screen shots of the ACATS graphical user interface and operations animation frame.

B-10 Evaluating Airfield Capacity B-10 6.4 ACES 6.4.1 National Aeronautics and Space Administration (2004) . Airspace Concept Evaluation System [PDF d ocument]. Retrieved from http://vams.arc.nasa.gov/pubs/ACES_FactSheet_100704.pdf This is a one page fact sheet about the Airspace Concepts Evaluation System (ACES) , an agent based mode l and simulation environment developed at the NASA Ames Research Center . “ This modeling approach isolates the individual models so they can continue to be enhanced, improved, and modified to represent new concepts with low development impact on the overall simulation system.” It also lists various examples of ACES usage . 6.4.2 Sweet, D., V. Manikonda, J. Aronson, K. Roth, & M. Blake ( 2002 ) . Fast - Time Simulation System for Analysis of Advanced Air Transportation Concepts. Proceedings of the AIAA Modeling an d Simulation Technologies Conference and Exhibit ( AIAA 2002 - 4593 ) , Monterey , CA . This is one of the earliest papers to present the ACES conceptual plan and discuss prototypes. The initial e mphasis was to be on the development and validation of a toolbox of compatible models that can be configured to address many different concep ts and evaluation criteria. The technologies for run - time communication between agents, data initialization and storage, and data access are presented. Additionally, lessons learned from prototype development are presented. 6.4.3 Couluris, G.J., C.G. Hunter, M. Blake, K. Roth, D. Sweet, P. Stassart, &A. Huang (2003). National Airspace System Simulation Capturing the Interactions of Air Traffic Management and Flight Trajectories. Proc eedings of the AIAA Guidance, Navigation, and Control Conference ( AIAA 2003 – 5597 ) , Austin , TX . The paper presents information about ACES and the underlying modeling concepts used. “The simulation accounts for terminal gate pushback and arrival, taxi, runwa y system takeoff and landing, local approach and departure, climb and descent transition, and cruise operations. ACES employs a multi - trajectory based modeling approach that currently models Traffic Flow Management, Air Traffic Control and Flight operation s, en route winds, and airport operating conditions. ” “The ACES tool applies a continual feedback, hierarchical modeling process to capture actions and responses among scheduling and trajectory planning, flight deck trajectory manageme nt, Traffic Flow Management strategic trajectory planning, and Air Traffic Control tactical trajectory management operations . The intent is to quantitatively describe air traffic movement resulting from the interaction of the operational and technological constructs .” 6.4.4 Zelinski, S., & T. Romer (2004) . An Airspace Concept Evaluation System Characterization of National Airspace System Delay. Proceedings of the 4th AIAA Aviation, Technology and Operations Conference (AIAA 2004 - 6200) , Chicago , IL. The re search highlighted in this paper utilize s the ACES simulation tool to establish an initial characterization of National Airspace System (NAS) – wide delay. It provides some details about the various ACES subroutines and their methodologies. ACES 1.2 , which w as used in this study , does not include sector capacity limits, separation constraints, flight plan rerouting, delay s in the arrival terminal area and arrival surface, and en - route altitude and cruise speed changes. The paper also highlights options availa ble in ACES 1.2, which are delay maneuvers and TRACON departure fix separation. “ Delay maneuvers are lateral en route course altera tions used to delay individual fl ights. The TRACON departure fi x separation option provides a simulation of miles - in - trail se paration of aircraft at each

Appendix B B-11 B-11 departure fi x. ” The ACES tool models an airport’s state per 15 min utes of run - time as either VFR or IFR, which in turn helps to define the airport’s capacity. 6.4.5 Meyn, L.A., T.F. Romer, K. Roth, & L.J. Bjarke (2004) . Prelimi nary Assessment of Future Operational Concepts Using the Airspace Concept Evaluation System. Proceedings of the 4th AIAA Aviation, Technology and Operations Conference ( AIAA 2004 - 6508 ) , Chicago , IL . This paper also provi des descriptions about ACES 1.2 and the various models within the ACES architecture. The agents in ACES assess projected demand over planning horizons, develop traffic flow plans , and issue traffic restrictions to other agen ts. The ATC agents within the si mulation manage tactical flight movement by applying standard operating procedures subject to the t raffic f low m anagement (TFM) agent restrictions. The model allows four degrees of freedom to emulate the movement of each aircraft along a four - dimension tra jectory in conformance with its current flight plan and clearance . The tool “ treats the runway system node as the critical factor in modeling each terminal operation. In this model ing structure, each Airport TFM agent invokes its model to examine projected takeoff and landing traffic loading at the runway system based on the flight schedule .” 6.4.6 Zelinski, S. (2005) . Validating the Airspace Concept Evaluation System Using Real World Data. Proceedings of the 5th AIAA Aviation, Technology and Operations Co nference (AIAA 2005 - 6491) , San Francisco , CA. This paper presents a case for environmental validity of the ACES tool using real - world historical fl ight operational data. The steps in preprocessing the input files are discussed. The inputs were select , act ual single day's operations within the National Airspace System. The output indicate d that the ACES produced delays and airport operational metrics were similar to the real world , with minor v ariations of delay by phase of fl ight. The paper also highlights the unintentional interaction between the generic nodal airport model and departure meter fix separation model within ACES . The paper does not mention which version/build was used in the validation. 6.4.7 Wieland. F. (2010). Advanced NextGen Algorithm in ACES: DAC, CNS.. . [PowerPoint Slides]. Retrieved from http://catsr.ite.gmu.edu/NASWideSim2/Wieland - ACESSystemWideModelingPresentation.pptx . This presentati on provides details about the ACES software architecture and the algorithms. The time required to simulate 1x traffic (nearly 50,000 flights) with a 3x4 quad 2.33 Hz Intel processor and 8GB memory is 90 minutes . Using the same hardware, the time required t o complete a simulation for 3x traffic (nearly 150,000 flights) is 6 hours. The algorithms discussed in the presentation include voice/datalink models, message propagation, navigation, surveillance, and the automatic slicing algorithm for dynamic airspace units. The architecture for time - based merging and separation, separation assurance framework, and multi aircraft batch simulation tool also are presented. The latest version/build number is not presented. 6.5 ADSIM 6.5.1 Federal Aviation Administration (2 010) . ADSIM (Airport Delay Simulation Model) . Retrieved from http://www.tc.faa.gov/acb300/adsim.asp . 6.6 DELAYSIM 6.6.1 Flight Transportation Associates (2001) . DELAYSIM . Retrieved from http://www.ftausa.com/delaysim.htm .

B-12 Evaluating Airfield Capacity B-12 6.7 ENPRAS 6.7.1 Flight Transportation Associates (2001) . ENPRAS . Retrieved from http://www.ftausa.com/enpras.htm . 6.8 FLA PS 6.8.1 Flight Transportation Associates (2001) . FLAPS . Retrieved from http://www.ftausa.com/flaps.htm . 6.9 FTA Models 6.9.1 Flight Transportation Associates (2001) . Modeling . Retrieved from http://www.ftausa.com/ftamodel.htm . From the webpage, clicking on the internal link or scrolling down to the heading “FTA Analysis Tools and Models” leads to paragraph descriptions of DELAYSIM, FLAPS, TASIM, and TAXSIM. In addition to these tools, descriptions are included for the following: 6.9.1.1 DELAYS 6.9.1.2 GATESIM 6.9.1.3 RUNCAP 6.9.1.4 LANDSIDE 6.9.1.5 TOPSIM 6.9.1.6 GNPM 6.9.1.7 FLEETASSIGN 6.10 JSIMMOD 6.10.1 AirportTools (2006) . AirportTools: Measuring Capacity Using JSIMMOD . Retrieved from http://www.airporttools.com/jsimmod/Documentation/other/capacity/index.html . 6.11 LMINET 6.11.1 Long, D., V. Stouffer - Costonn , P. Kostiuk, R. Kula, & B. Fernandez (2001). Integrating LMINET with TAAM and SIMMOD (NASA Publication No . NASA/CR - 2001 - 210875). Langley Research Center, Hampton , VA: National Aeronautics and Space Administration. This report highlights details of the LMINET model as well as integration issues. “ LMINET is a queuing network model of the entire National Airspace System (NAS) developed by LMI for NASA ” and “ models flights among a set of airports by linking queuing network models of airports with sequences of queuing models of the Terminal Radar Approach Control (TRACON) and Air Route Traffic Control Center (ARTCC) sectors. ” “ LMINET can generate the average delay for all departures and arrivals during each epoch at each LMINET airport .” 6.11.2 Long, D., & H. Hasan (2009) . Improved Prediction of Flight Delays Using the LMINET2 System - Wide Simulation Model. Proceedings of the 9th AIAA Aviation Technology, Integration, and Operations Conference , Hilton Head , SC.

Appendix B B-13 B - 13 6.12 NASPAC 6.12.1 Federal Aviation Administrati on (2010) . ACB - 330/NASPAC . Retrieved from http://www.tc.faa.gov/acb300/330_naspac.asp . At time of publication a functioning link to information about NASPAC was available at http://www.faa.gov/about/office_org/headquarters_offices/ang/offices/ac_td/at_sys_con _dev/sim_analysis_team/models/naspac/. 6.13 RAMS 6.13.1 ISA Software (2010) . RAMS Plus Simulation Solutions [PDF d ocument]. Retrieved from http://www.ramsplus.com/files/What%20Is%20RAMS%20Plus.pdf . 6.14 RDSIM 6.14.1 Federal Aviation Administration (2010) . RDSIM (Runway Delay Simulation Model) . Retrieved from http://www.tc.faa.gov/acb300/rdsim.asp . 6.15 REDIM 6.15.1 Virginia Tech: Air Transportation Systems Laborat ory (2010) . REDIM 2.0 User’s Manual [PDF d ocument]. Retrieved from http://www.atsl.cee.vt.edu/Redim/Redim_2_Manual.pdf . 6.16 SIMMOD 6.16.1 Federal Aviation Administration (2010) . Simmod Manual: How Simmod Works [PDF d ocument]. Retrieved from http://www.tc.faa.gov/acb300/how_simmod_works.pdf . 6.17 SIMMOD PLUS 6.17.1 ATAC Corporation (2009) . ATAC Products & Tools: Simm od PLUS! Information originally r etrieved from http://www.atac.com/Products_Airports - b.html . 6.18 SIMMOD PRO 6.18.1 ATAC Corporation (2009) . ATAC Products & Tools: Simmod PRO! Retrieved from http://www.atac.com/simmod - pro.html . 6.19 TAAM 6.19.1 Jeppesen (2010) . Jeppesen Total Airspace and Airport Modeler (TAAM) . Retrieved from http://www.jeppesen.com/industry - solutions/aviation/government/total - airspace - airport - modeler.jsp;jsessionid=Lh2XDWnpgNPnH1BjrCvCfhpBNGhCRR6hX4g7s1C2ZJW8Sg26n6n 0! - 1409014597 . 6.19.2 Preston Aviation Solutions Pty Ltd (2005) . TAAM 2.3 Reference Manual (Document 11.001 - 06). Available from Preston Aviation Solutions Pty Ltd., Australia. 6.19.3 Boesel, J., C.X. Galdstone, J. Hoffman, P.A. Massimini, C. Shiotsuki, & B. Simmons (2001) . TAAM Best Practices Guidelines . McLean, VA: The MITRE Corporation.

B-14 Evaluating Airfield Capacity B - 14 6.20 TASIM 6.20.1 Flight Transportation Associates (2001) . TASIM. Retrieved from http://www.ftausa.com/tasim.htm . 6.21 TAXSIM 6.21.1 Flight Transportation Associates (2001) . TAXSIM. Retrieved from http://www.ftausa.com/taxsim.htm . 6.22 The Airport Machine 6.22.1 Massachusetts Institute of Technology (1996) . The Airport Machine Review . Retrieved from http://web.mit .edu/aeroastro/www/labs/AATT/reviews/airportmachine.html . 6.23 TSAM 6.23.1 Baik, H., & Trani, A.A. (2005). A Transportation Systems Analysis Model (TSAM) to Study the Impact of the Small Aircraft Transportation System (SATS) [PDF poster documentation]. Ret rieved from http://www.atsl.cee.vt.edu/Publications/2005_A_Transportation_Sys tems_Analysis_Mo del_TSAM_to_study_the_impact_of_the_Small_Aircraft_Transportation_System_SATS. pdf . 6.24 VTASIM 6.24.1 Trani, A., & H. Baik (2002). VTASIM: A New Paradigm to Model Airport Operations. In : J. Rakas & S.A. Mumayiz ( e ds.) . Transportation Resear ch E - Circular (E - C042). Retrieved from TRB Publications Index. 6.25 Boeing Airport Capacity Constraints Model 7. Government and University Research Documents and Papers 7.1 Hockaday, S. & A.K. Kanafani (1974). Developments in Airport Capacity Analysis. Transportation Research , 8(3), 171 – 80. This paper presents a model to calculate the runway capacity. The model assumes that the aircraft deviations (from intended paths) are normally distributed random variables. The model allows selection of strategies f or actual and intended arrival - departure mix and also accounts for effects of wake turbulence. The model includes three mains steps. First, the time intervals between landing and departure operations are calculated. These intervals are then manipulated to produce capacity estimates using one (or combination s of) operating strategies, which include arrivals only, departures only , and mixed operations. Selection of the operating strategy that yields highest capacity is the final step. 7.2 Newell, G.F. (1979) . Airport Capacity and Delays. Transportation Science , 13(3), 201 – 41. This paper presents literature review of airport capacity. It highlights the dependence of airport capacity on types of operations, the runway geometry , and flight rules. Of particular i nterest are sections 5.a through 5.d , which discuss the airport capacity for various arrival/departure rates and its boundary curve for a single runway. The paper also discusses two and three parallel runway configurations.

Appendix B B-15 B - 15 7.3 Hansen, M., T. Nikoleris, D. Lovell, K. Vlachou & A Odoni (2009). Use of Queuing Models to Estimate Delay Savings from 4d Trajectory Precision . Proceedings of the 8th U . S . A . /Europe Air Traffic Management Research and Development Seminar , Napa , CA. The authors compare predicted values of delays using three queuing models across a range of demand and capacity scenarios at seven major U . S . airports. They estimate that simply better prediction of delays, without change in capacity, could reduce delays by 35% when the baseline delay is around 6 minutes. The difference in average delay predicted was found to be well - approximated as a constant on the order of 1 minute and a fraction of the stochastic delay on the order of 10%. 7.4 Mosquera - Benitez, D., A.R . Groskreutz, & L. Fucke (2009) . Separation Minima Model . Proceedings of the 8th U . S . A . /Europe Air Traffic Management Research and Development Seminar , Napa , CA. This paper presents a tool to compar e and understand the effect on separation minima. The stud y compiled 622 separation minima standards . They found only 15% of the cases listed contributing factors while 49% of the cases listed no factors . A model for calculating horizontal separation minima is developed. 7.5 Kim, A., & M. Hansen (2009) . Validation of Runway Capacity Models. Proceedings of the 8th U . S . A . /Europe Air Traffic Management Research and Development Seminar , Napa , CA. This paper introduce s two methodologies for validating capacity model results agains t empirical data. Their result s indicate that the Airfield Capacity Model ( ACM ), developed by FAA and MITRE CAASD, and runway Simulator ( rS ), developed by MITRE CAASD, predict greater differences between average VMC and IMC ca pacity than do actual data . The models appear ed to have over - predict ed VMC capacities. The ir results also indicate that the two models predict ed wider ranges of capacities . Of the two models compared, the authors found rS model estimates to be better . 7.6 Trani, A.A. (2009) . Modeling and Simulation Tools for NextGen: A Few Missing Links [PDF p resentation slides]. Retrieved from http://www.nasug.com/200903/NASUG_VT_Spring2009.pdf . This presentation highlights the gaps found in the simulation tools for NextGen. The focus is primarily on wake vortex simulation tools and 4D trajectories. It seems that this presentation is an outcome from the LMI report discussed previously ( Item 6.1). 7.7 Klein, A., S. Kavoussi, & R.S. Lee (2009). Weather Forecas t Accuracy: Study of Impact on Airport Capacity and Estimation of Avoidable Costs. Proceedings of the 8th U . S . A . /Europe Air Traffic Management Research and Development Seminar , Napa , CA. The authors present a model to quantify the impact of forecast weathe r on the NAS . The results of model - based arrival rates for 35 airports under a wide variety of weather conditions were compared against actual data and were found to be valid. The model can estimate the avoidable arrival delays attributable to terminal wea ther forecast , by specific weather factor. The authors estimate the a nnual cost of avoidable arrival delays related to terminal weather forecast is approximately $330M. 7.8 Jeddi, B., & J. Shortle (2007) Throughput, R isk, and E conomic O ptimality of R unway L anding O perations. Proceedings of the 7th U . S . A . /Europe Air Traffic Management Research and Development Seminar , Barcelona , Spain, p. 162. This paper proposes an optimization model to maximize successful landings on a single runway while mitigating wake - vortex encounter and simultaneous runway occupancy risks . “T he risks are mitigated by

B-16 Evaluating Airfield Capacity B - 16 enforcing go - around procedures when separation distances are too small. ” The authors also propose two tools . The first maximizes the risk - free throughput (number of succe ssful landings per unit of time) with and without wake - vortex effects. The second maximizes expected net economic outcome (total dollar benefits minus total go - around costs) by adjusting the rate of landing attempts. 7.9 Shortle, J., & B. Jeddi (2007). Us ing Multilateration Data in Probabilistic Analysis of Wake Vortex Hazards for Landing Aircraft. Transportation Research Record , pp. 90 – 6. A method to estimate wake alert probabilities based on a direct feed of flight - track data is provided . The model allo ws v ar iation in atmospheric parameters to evaluate the potential range of wake alert probabilities. They found that under certain conditions a decrease in wake alert probability is not observed even if the wake can d issipate quickly. T he wakes were found to remain for a longer period of time a t a higher altitude before d issipating below a critical threshold. This effect becomes more pronounced with higher wake thresholds. 7.10 Byung, J.K., A. Trani, X. Gu, & C. Zhong (1996) . Computer Simulation Model for Airplane Landing Performance Prediction . Transportation Research Record , 1562, pp. 53 – 62. This paper presents a model to predict airplane landing performance on runways to locate high - speed exits . The landing process is considered to be of five p arts : flare, first free roll, braking, second free roll, and turnoff . The authors found that t he landing distance fo r a group of transport aircraft is probabilistic with a large dispersion . They also confirmed that runway length has a strong influence on t he touchdown l ocation in transport operations. The deceleration rates found their observ ational study indicated aircraft decelerate well below their maximum capabilities . The deceleration rate has a weak correlation with the flare (one of the five parts of the landing process) speed and the length of runway available for braking . 7.11 Zhang, Y., J. Rakas, & E. Liu (2006). Methodology for Estimating Airport Capacity and Throughput Performance U sing PDARS. Proceedings of the 10th Air Transport Research Society (ATRS) World Conference , Nagoya, Japan. This paper reports a method of assessing performance and proposes efficiency metrics for runway and airport utilization. The authors use normal - lognormal probability distribution for landing time intervals . They found that “ each airport has a unique probability distribution for arrivals, depending on the number and complexity of runway layouts and runway configurations in use, weather conditions, traffic demand, aircr aft mix , or air traffic control ‘ culture ’ deployed at an airport. ” A single - effect model with linear functions of major parameters (the target separation and the arrival rate) is proposed . The study confirmed that c apacity variation occurs among different runway configurations . 7.12 Kumar, V., & L. Sherry (20 08 ). Airport Throughput Capacity Limits for Demand Management Planning [PDF document]. Retrieved from http://catsr.ite.gmu.edu/pubs/ICNS_Kumar_Sherry.pdf . This paper evaluates the variability of throughput capacity at the OEP - 35 airports during the convective weather season in 2008 . Thirteen airports showed a reduction of more than 20% in capacity more than 10% of the time. The paper helps establish the average costs of delays due to reduced capacity and the average profits per flight at each of the QEP - 35 airport s. Twenty - four airports exhibit ed an average cost of delays per flight in excess of the average profit generated by a flight . The authors also identif y that the “o ptimum airport capacity, computed by trading - off flights delays and underutilization ranged from 81% to 100% of the maximum airport capacity. The average optimum airport capacity was 93% of the maximum airport capacity. ”

Appendix B B-17 B-17 7.13 Gaier, E.M., & P.F. Kos�uk (1998). Evalua�ng the Economic Impact of ATM Innova�ons on Commercial Air Carrier Opera�ons. Proceedings of the 2nd U.S.A./Europe Air Traffic Management Research and Development Seminar, Orlando, FL. This paper discusses the Air Carrier Cost-Benefit Model (CBM), an analysis tool to support credible es�mates of benefits to commercial airline operators from proposed technical and procedural innova�ons. This paper also analyzes the specific capacity-enhancement program Low Visibility Landing and Surface Opera�ons (LVLASO), part of the NASA Terminal Area Produc�vity (TAP) Program. LVLASO seeks to augment exis�ng airport capacity by reducing aircra� runway occupancy �me, separa�on requirements, and taxi �mes in low visibility condi�ons. LVLASO is modeled using airport capacity and delay models and their results indicated modest benefits of LVLASO for commercial operators with substan�al risk. 7.14 Jeddi, B., J. Shortle, L. Sherry (2006). Sta�s�cal Separa�on Standards for the Aircra�-Approach Process. Proceedings of the 25th Digital Avionics Systems Conference (2A1-1–2A1-13). 7.15 Levy, B., J. Legg, and M. Romano (2004). Opportuni�es for Improvements in Simple Models for Es�ma�ng Runway Capacity. Presented at the 23rd Digital Avionics Systems Conference, Salt Lake City, UT. 7.16 Lee, D.D., A. Smith, R. Cassell, & B. Abdul-Baki (1999). NASA Low Visibility Landing and Surface Opera�ons (LVASO) Runway Occupancy Time (ROT) Analysis, IEEE 0-7803-5749-3/99. 7.17 Wieland, F. (2006). Inves�ga�ng the Volume-Delay Rela�onship at Congested Airports. Proceedings of the 6th AIAA Avia�on, Technology and Opera�ons Conference (AIAA 2006- 7747), Wichita, KS. 7.18 Andrews, J., & J.E. Robinson (2001). Radar-based Analysis of Efficient Runway Use. Proceedings of the AIAA Guidance, Naviga�on and Control Conference, Montreal, Quebec. 7.19 MIT Lincoln Laboratory (1993). Evalua�on of the Capacity and Delay Benefits of Terminal Air Traffic Control Automa�on (Report ATC-192). Lexington, MA: S.B. Boswell

B-18 Evaluating Airfield Capacity B-18 DATA SOURCES This sec�on lists sources of informa�on about each of the factors that influence capacity es�mates. Addi�onally, more detailed descrip�ons are included for sources for track data, aircraft counts, and runway occupancy �me. Runway Exit Design, Runway Entrance Taxiways, Departure Staging and Sequencing of Taxiways or Areas, Runway Crossings, Parallel Taxiway Data Requirements Descrip�on 1. Sources FAA airport diagrams Google maps of airports 2. Availability Publicly available 3. Type Scaled maps and images 4. Format Electronic and paper 5. Age Recent 6. Volume N/A 7. Cost No cost 8. Post-processing N/A 9. Limita�ons N/A 10. Default values N/A Airline Fleet Mix, Airline Scheduling Prac�ces Data Requirements Descrip�on 1. Sources BTS—AOTP database FAA—ASQP/ASPM database 2. Availability BTS—AOTP database (publicly available) FAA—ASQP/ASPM database (credible en��es may apply to FAA for access to the database) 3. Type Historical data, tables 4. Format Electronic 5. Age Period/season under inves�ga�on 6. Volume Week or month 7. Cost No cost 8. Post-processing None 9. Limita�ons N/A 10. Default values N/A

Appendix B B-19 B-19 Aircra� Avionics Equipage Data Requirements Descrip�on 1. Sources Forecast equipage profiles for fleets (aircra� manufacturers order lists, FAA, Mitre CAASD) 2. Availability Publicly available 3. Type Historical data and forecast data 4. Format Electronic, paper 5. Age N/A 6. Volume N/A 7. Cost N/A 8. Post-processing None 9. Limita�ons N/A 10. Default values N/A Aircra� Performance Random Variability, Human Factors Random Variability Data Requirements Descrip�on 1. Sources TRACON/surface track data 2. Availability FAA-funded equipment, then FAA proprietary Airport- or airline-funded equipment, then available from source 3. Type Historical data (requires post-processing) 4. Format Electronic 5. Age N/A (Note: important to cover all runway/arrival fix/departure fix configura�ons) 6. Volume Approaches and departures for each runway 7. Cost N/A 8. Post-processing Track data must be processed to es�mate impact 9. Limita�ons N/A 10. Default values 10 seconds ATC buffer

B-20 Evaluating Airfield Capacity B-20 Visual Flight Rules and Visual Approaches Data Requirements Descrip�on 1. Sources Charts— – Standard Terminal Arrivals (STARS), Instrument Approach Procedures (IAP), Departure Procedures (DP). 2. Availability FAA (alterna�ve sources: airnav.com, skyvector.com) 3. Type Aeronau�c charts 4. Format Electronic or paper 5. Age Check for latest/planned revision 6. Volume N/A 7. Cost No cost 8. Post-processing None 9. Limita�ons N/A 10. Default values N/A Weather Data Requirements Descrip�on 1. Sources FAA—ASPM database FAA—TAF database 2. Availability FAA—ASPM database (credible en��es may apply to FAA for access to the database) FAA—TAF database 3. Type Historical data, tables, 4. Format Electronic 5. Age Period/season under inves�ga�on 6. Volume Week or month 7. Cost No cost 8. Post-processing None 9. Limita�ons N/A 10. Default values N/A

Appendix B B-21 B - 21 Wake Turbulence Data Requirements Description 1. Sources U.S. Department of Transportation, Federal Aviation Administration Air Traffic Organizational Policy JO 7110.65U 2. Availability Publicly a vailable http://www.faa.gov/documentLibrary/media/Order/ATC.pdf 3. Type Regulations 4. Format Electronic 5. Age Most recent 6. Volume N/A 7. Cost No c ost 8. Post - processing None 9. Limitations N/A 10. Default values N/A Multiple Approach Technology Data Requirements Description 1. Sources TRACON/ s urface track data 2. Availability FAA - funded equipment, then FAA proprietary Airport - or airline - funded equipment, then available from source 3. Type Historical data (requires post - processing) 4. Format Electronic 5. Age N/A (Note: important to cover all runway/arrival fix/departure fix configurations) 6. Volume Multiple approach 7. Cost N/A 8. Post - processing Track data must be processed to estimate impact 9. Limitations N/A 10. Default values Excess distance flown, runway throughput

B-22 Evaluating Airfield Capacity B-22 Human Factors (Controller Workload), Human Factors (Air-Ground Communica�ons) Default values: 8 seconds to 30 seconds, depending on traffic count and complexity of communica�on. Neighboring Airports (STARs, Approaches and SIDS)—Used in Conjunc�on with Published Procedures Data Requirements Descrip�on 1. Sources 2. Availability Publicly available 3. Type Standards 4. Format Electronic naviga�on database and paper 5. Age Latest revisions 6. Volume N/A 7. Cost Nominal fee 8. Post-processing Analysis required to overall the procedures and see intersections 9. Limita�ons This analysis will show the published procedure intersec�ons. Must use track data to see devia�ons from procedures. 10. Default values N/A Departure Fix Restric�ons—Derived from Throughput at Fixes from Track Data Data Requirements Descrip�on 1. Sources TRACON/surface track data 2. Availability FAA-funded equipment, then FAA proprietary Airport- or airline-funded equipment, then available from source 3. Type Historical data (requires post-processing) 4. Format Electronic 5. Age N/A (Note: important to cover all runway/arrival fix/departure fix configura�ons) 6. Volume Mul�ple runway/arrival fix/departure fix configura�ons 7. Cost N/A 8. Post-processing Track data must be processed to es�mate departure fix throughputs 9. Limita�ons N/A 10. Default values Count of flights per 15 minutes at each fix for each runway/arrival fix/departure fix configura�ons

Appendix B B-23 B-23 Neighboring Airports (Radar Track Data)—Used in Conjunc�on with Published Procedures Data Requirements Descrip�on 1. Sources TRACON/surface track data. 2. Availability FAA-funded equipment, then FAA proprietary Airport- or airline-funded equipment, then available from source 3. Type Historical data (requires post-processing) 4. Format Electronic 5. Age Check naviga�on charts for revision dates (older data may no longer be relevant) (Note: important to cover all runway/arrival fix/departure fix configura�ons) 6. Volume Mul�ple runway/arrival fix/departure fix configura�ons 7. Cost N/A 8. Post-processing Track data must be processed to iden�fy intersec�ng trajectories 9. Limita�ons N/A 10. Default values Count of intersec�ng trajectories Missed Approach and Balked Landing Procedures Data Requirements Descrip�on 1. Sources TRACON/surface track data Air naviga�on charts 2. Availability FAA radar data collected on FAA-funded equipment (therefore FAA proprietary) Airport- or airline-funded equipment, then available from source (FAA will limit access to data based on na�onal security requirements) 3. Type Historical data (requires post-processing) 4. Format Electronic 5. Age N/A (but if charted procedures have changed, older data may not be relevant) (Note: important to cover all runway/arrival fix/departure fix configura�ons) 6. Volume Mul�ple runway/arrival fix/departure fix configura�ons (need mul�ple days on each configura�on to capture a significant volume of missed approach data) 7. Cost N/A 8. Post-processing Track data must be processed to count go-arounds 9. Limita�ons N/A 10. Default values Count of missed approaches less than five per day at major airports

B-24 Evaluating Airfield Capacity B - 24 Track Data This section describes the two sources of track data available in the United States : Performance Data Analysis and Reporting System (PDARS) and Airport Surface Detection, Model X (ASDE - X). Performance Data Analy sis and Reporting System PDARS data and measurements are based on the processing of radar track and flight plan data collected from Automatic Radar Terminal System (ARTS) computers at the TRACONs, and data collected from the h ost computers at the ARTCCs. Th ese data provide mo r e de tails of tracks flown than the Enhanced Traffic Management System (ETMS) or its commercial ASD Feed (used in the industry). Figure B - 1. Departure tracks for departures from Phoenix - Sky Harbor Runways 26L/R (den Braven & Schade, 2003). The type s of information generated from PDARS data include: • • • • • • Travel times within geometric areas (sectors, facilities, etc.) Travel times for routing segments (arrival fix to runway, runway to departure fix, facility boundary to/from runway, etc.) Flow counts over u ser - defined points Throughput counts for airports, sectors, etc. Groundspeed distributions at user - defined times and areas Identification of aircraft deviating from a prescribed procedure

Appendix B B-25 B-25 PDARS data can be used to iden�fy and generate sta�s�cs for events, such as sector crossings, as seen in Figure B-2. Figure B-2. Sector crossings. PDARS data are available from the following facili�es: Western Pacific Region: • Oakland Center (ZOA) • Los Angeles Center (ZLA) • Northern California TRACON (NCT) • Southern California TRACON (SCT) • Phoenix TRACON (P50) • Western Pacific Regional Office (AWP) Southwest Region: • Albuquerque Center (ZAB) • Houston Center (ZHU) • Fort Worth Center (ZFW) • Dallas/Fort Worth TRACON (D10) • Houston TRACON (I90) • Southwest Regional Office (ASW) Southern Region: • Jacksonville Center (ZJX) • Memphis Center (ZME) • Atlanta Center (ZTL) • Miami Center (ZMA) Great Lakes Region: • Indianapolis (ZID)

B-26 Evaluating Airfield Capacity B-26 Na�onal: • ATC System Command Center (ATCSCC) in Herndon, Virginia Format A sample format of PDARS data is shown below. 1,BFF,2.6 5,ATAC Corpora�on,BirdWatch Analysis Module,4.5.5 0, 2(Record Type),1205894410.240(Time),52533(Flight ID),2723(Beacon Code),721(Unknown),0/JFK(Original Airport),,CAL011(Aircra� ID),1,B744(Aircra� Type),JFK(Original Airport),GAY(Des�na�on Airport),D(Opera�on Type),JFK,? 4,1205894410.240,52533,2723,721,0/JFK,1353,CAL011,1,B744,JFK,GAY,N,-99.00,-99.00,340,,?,- 00099,I,J,D,?,-099,?,5,,,,,, 4,1205901708.450,52533,2723,721,3/HPN,1353,CAL011,1,B744,JFK,GAY,N,-99.00,-99.00,340,,?,- 00099,I,J,D,?,-099,?,5,,,,,, 3(Record Type),1205901651.730(Time),52533(Flight ID),2723(Beacon Code),721(Unknown),0/JFK(Original Airport),1350,CAL011(Aircra� ID),1,40.63518(La�tude),- 73.78761(Longitude),4.76(Al�tude *100),1,0.003,0.003,-99.00,153(Speed),104,2004,?,?,?,-99,,- 99,,,,,,0,48,0,?,2,A,JFK 3,1205901660.714,52533,2723,721,0/JFK,1350,CAL011,1,40.63221,-73.78042,7.76,3,0.002,0.002,- 99.00,153,104,2004,?,?,?,-99,,-99,,,,,,0,48,0,?,2,A,JFK 3,1205901669.583,52533,2723,721,0/JFK,1350,CAL011,1,40.62907,-73.77326,9.85,1,0.002,0.002,- 99.00,158,106,1807,?,?,?,-99,,-99,,,,,,0,48,0,?,2,A,JFK 3,1205901673.996,52533,2723,721,0/JFK,1350,CAL011,1,40.62801,-73.76911,10.76,2,0.002,0.002,- 99.00,166,94,1237,?,?,?,-99,,-99,,,,,,0,48,0,?,2,A,JFK 3,1205901678.408,52533,2723,721,0/JFK,1350,CAL011,1,40.62786,-73.76476,12.76,4,0.002,0.002,- 99.00,166,78,1593,?,?,?,-99,,-99,,,,,,0,48,0,?,2,A,JFK 3,1205901682.838,52533,2723,721,0/JFK,1350,CAL011,1,40.62761,-73.76026,13.76,10,0.002,0.002,- 99.00,168,80,1564,?,?,?,-99,,-99,,,,,,0,48,0,?,2,A,JFK 3,1205901691.794,52533,2723,721,0/JFK,1350,CAL011,1,40.62745,-73.75134,16.76,1,0.003,0.003,- 99.00,171,77,1564,?,?,?,-99,,-99,,,,,,0,48,0,?,2,A,JFK 3,1205901696.315,52533,2723,721,0/JFK,1350,CAL011,1,40.62742,-73.74665,17.76,9,0.004,0.004,- 99.00,173,76,1332,?,?,?,-99,,-99,,,,,,48,50,2,K,130,D,JFK

Appendix B B-27 B-27 3,1205901700.860,52533,2723,721,0/JFK,1350,CAL011,1,40.62749,-73.74191,17.76,9,0.004,0.004,- 99.00,176,75,1318,?,?,?,-99,,-99,,,,,,48,50,2,K,130,D,JFK 3,1205901705.420,52533,2723,721,0/JFK,1350,CAL011,1,40.62751,-73.73702,18.76,5,0.005,0.005,- 99.00,177,75,1310,?,?,?,-99,,-99,,,,,,48,56,2,K,130,D,JFK Record-type = 2 denotes a flight-header record in PDARS Record-type = 3 denotes a posi�on record Airport Surface Detec�on, Model X ASDE-X data provides track and iden�fica�on data for aircra� and equipped vehicles on the airport’s surface as well as aircra� within approximately 5 miles of the airport. The core ASDE-X track data includes a radar sensor and a mul�latera�on sensor capable of receiving Automa�c Dependent Surveillance—Broadcast (ADS-B) from the aircra�. The ASDE-X Mul�processor subsystem combines independent surveillance data from the radar subsystem with coopera�ve and dependent data from the mul�latera�on subsystem to provide a single target with Flight ID for display to air traffic control. The mul�processor subsystem may also integrate data from Automated Radar Terminal System (ARTS) and the Standard Terminal Automa�on Replacement System (STARS) as well as the Airport Surveillance Radar (ASR), dependent on the implementa�on chosen for a given site. The system is capable of using all sensors at once, or using each sensor alone. The 35 airports scheduled to have ASDE-X installa�on are listed below. Airports with ASDE-X opera�ng as of July 2010 are shown with an asterisk. • Bal�more-Washington Interna�onal Thurgood Marshall Airport (Bal�more, MD) • Boston Logan Interna�onal Airport (Boston, MA)* • Bradley Interna�onal Airport (Windsor Locks, CT)* • Chicago Midway Airport (Chicago, IL)* • Chicago O’Hare Interna�onal Airport (Chicago, IL)* • Charlo�e Douglas Interna�onal Airport (Charlo�e, NC)* • Dallas-Ft. Worth Interna�onal Airport (Dallas, TX)* • Denver Interna�onal Airport (Denver, CO)* • Detroit Metro Wayne County Airport (Detroit, MI)* • Ft. Lauderdale/Hollywood Airport (Ft. Lauderdale, FL)* • General Mitchell Interna�onal Airport (Milwaukee, WI)* • George Bush Intercon�nental Airport (Houston, TX)* • Hartsfield-Jackson Atlanta Interna�onal Airport (Atlanta, GA)* • Honolulu Interna�onal –Hickam Air Force Base Airport (Honolulu, HI)* • John F. Kennedy Interna�onal Airport (Jamaica, NY)* • John Wayne-Orange County Airport (Santa Ana, CA)* • LaGuardia Airport, (Flushing, NY) • Lambert-St. Louis Interna�onal Airport (St. Louis, MO)* • Las Vegas McCarran Interna�onal Airport (Las Vegas, NV) • Los Angeles Interna�onal Airport (Los Angeles, CA)* • Louisville Interna�onal Airport-Standiford Field (Louisville, KY)* • Memphis Interna�onal Airport (Memphis, TN)

B-28 Evaluating Airfield Capacity B-28 • Miami Interna�onal Airport (Miami, FL)* • Minneapolis St. Paul Interna�onal Airport (Minneapolis, MN)* • Newark Interna�onal Airport (Newark, NJ)* • Orlando Interna�onal Airport (Orlando, FL)* • Philadelphia Interna�onal Airport (Philadelphia, PA)* • Phoenix Sky Harbor Interna�onal Airport (Phoenix, AZ)* • Ronald Reagan Washington Na�onal Airport (Washington, DC) • San Diego Interna�onal Airport (San Diego, CA)* • Salt Lake City Interna�onal Airport (Salt Lake City, UT)* • Sea�le-Tacoma Interna�onal Airport (Sea�le, WA)* • Theodore Francis Green State Airport (Providence, RI)* • Washington Dulles Interna�onal Airport (Chan�lly, VA)* • William P. Hobby Airport (Houston, TX)* Format A sample format from an ASDE-X system is shown below. HR MIN SEC X Y Height ACID AcType 09 59 21.000 6833 -16909 8068.75 DAL104 B764 09 59 22.000 6724 -16836 8056.25 DAL104 B764 09 59 23.000 6614 -16763 8037.5 DAL104 B764 09 59 24.000 6505 -16691 8018.75 DAL104 B764 09 59 25.000 6396 -16618 8000.0 DAL104 B764 09 59 26.000 6300 -16533 7856.25 DAL104 B764 09 59 27.000 6192 -16459 7818.75 DAL104 B764 09 59 28.000 6085 -16385 7787.5 DAL104 B764 09 59 29.000 5977 -16311 7750.0 DAL104 B764 09 59 30.000 5861 -16258 7662.5 DAL104 B764 09 59 31.000 5752 -16186 7625.0 DAL104 B764 09 59 32.000 5644 -16114 7593.75 DAL104 B764 Msg_Type Time ACID TrackNum AcType Lat/Long/Al�tude DD_TRACK 1183335341631 - 29 11152295 -1 - - N/A 325348.1198605895/970159.4251284003/575.0 677/-17 0.0 6.0 false -1 - 1183335342000 1009132 DD_TRACK 1183335341631 AAL1871 243 10595333 -1 - - MD82 325305.30234992504/970305.1284533739/975.0 -1031/-1336 -9.721148 -92.49054 false -1 - 1183335342000 1008970 DD_TRACK 1183335341631 - 213 9015376 -1 - - N/A 325350.6506253779/970333.3443534374/-1.0 - 1764/61 0.0 0.0 false -1 - 1183335342000 0 DD_TRACK 1183335341631 FW1 205 1 -1 - - HELO 324203.5883772373/971531.2270015478/1300.0 - 20468/-21701 1.4657788 41.974415 false -1 - 1183335342000 0

Appendix B B-29 B-29 DD_TRACK 1183335341631 UNKN 207 11025938 -1 - - N/A 325326.27447515726/970203.4371766448/- 1.0 572/-690 0.0 0.0 false -1 - 1183335342000 0 DD_TRACK 1183335341632 EGF670 244 1 -1 - - E135 324420.83295568824/971052.6947163045/11000.0 -13213/-17493 94.95285 91.69491 false -1 - 1183335342000 1009119 DD_TRACK 1183335341632 N88XJ 134 1 -1 - - BE9L 325204.63490590453/964530.7500444353/3000.0 26384/-3170 -95.03762 71.61599 false -1 - 1183335342000 0 DD_TRACK 1183335341632 EGF849 208 11207505 -1 - - E135 325416.91089332104/970248.739888072/-1.0 -605/870 -0.0 -0.0 false -1 - 1183335342000 1009101 Aircraft Count Data Three sources of aircra� count data are Avia�on System Performance Metrics (ASPM), Official Airline Guide (OAG), and Enhanced Traffic Management System Counts (ETMSC). These three databases are described below. Avia�on System Performance Metrics The Avia�on System Performance Metrics (ASPM) provides data on IFR flights to and/or from major airports (approx 77) and on all flights by specified carriers (approx 22). Flights include interna�onal and domes�c airports. The ASPM data includes: • Airport weather (VMC, MVMC, IMC) • Runway configura�on • Arrival rates • Departure rates OAG OAG Flight Guide (the OAG) is the complete printed reference on worldwide flight schedules. The OAG is updated monthly and lists full details of direct and connec�ng flights, transfer �mes, and flight rou�ngs. It is the only publica�on to cover global flight lis�ngs. The OAG also includes a wealth of supplementary informa�on, including industry codes, equipment types, and contact details for the world’s airlines and airports. Enhanced Traffic Management System Counts The Enhanced Traffic Management System Counts (ETMSC) is designed to provide informa�on on traffic counts by airport or by city pair for various data groupings (such as aircra� type or by hour of the day). Informa�on on oceanic flights, frac�onal ownership flights, or business jet ac�vity is also maintained. ETMSC source data are derived from filed flight plans and/or when flights are detected by the Na�onal Airspace System (NAS), usually via radar. ETMSC records are assembled by FAA’s Air Traffic Airspace (ATA) Lab by combining electronic messages transmi�ed to the host (en route) computer for each flight into a complete record of that flight. ETMSC has three views: Airport, City Pair, and Distributed OPSNET. It includes informa�on about commercial traffic (air carriers and air taxis), general avia�on traffic, and military air traffic to and from every landing facility, as well as fixes, both in the United States and in nearby countries that par�cipate in the ETMS system. Data for each month are made available to the

B-30 Evaluating Airfield Capacity B - 30 ETMSC data access system approximately 10 days after the end of the month. Preliminary next - day ETMS data and enhanced 5 - day data are used to construct ASPM records, but these preliminary data are not reported in the ETMSC data access system. Arrival and Departure Runway Occupancy Times 7.1) Definition Runway Occupancy Time (ROT) is a statistical distribution of the time aircraft occupy the runway. The time starts when the aircraft crosses the runway threshold and ends when the aircraft has cleared the runway (by more than x distance). Arrival runway occupancy time (AROT) begins when an arriving aircraft passes over the runway threshol d 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. Departure runway occupancy time (DROT) 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 th e opposite end of the runway. The starting and ending locations for measuring ROT are shown in Figure B - 3 by the X at time t 0 and the X at time t 1 . Figure B - 3. Boundaries Used for Estimation of ROT. ROT data typically appears in the form of a normal distribution (e.g., a bell - curve with mean = 45 seconds and standard deviation = 8 seconds). The ROT distribution is determined by fleet mix (i.e., landing speed), runway layout (e.g., high speed exits), and taxi instructions and ai rports surface flow (e.g., relative location of gates). 7.2) Data Used PDARS, ASDE - X

Appendix B B-31 B-31 7.3) Data Analysis Process Deriving the ROT distribu�on from surface track data requires a four-step process: Step 1: Iden�fy arrival tracks. The first step in the process is to parse the surface data to iden�fy tracks associated with arrivals. One approach is to sort the list of tracks by �me of day. Then use the first set of track points to es�mate the ini�al velocity. Tracks with a velocity greater than a threshold (e.g., 80 knots) can reliably be iden�fied as arrivals. Tracks with a velocity equal to or less than 80 knots at the threshold can be iden�fied reliably as departures. This rela�onship can be expressed as: If (VelocityTrackStart > 80 knots) then {Opera�on = Arrival} Else {Opera�on = Departure} Step 2: Iden�fy runway for each track. The nature of arrival opera�ons is that they pass directly over the runway threshold. This fact can be used to iden�fy which runway is being used. One approach is to examine the track data rela�ve to polygons that iden�fy each runway threshold. Another less processing-intensive approach is to examine the first batch of hits (e.g., n = 80) in each track of the tracks tagged as arrival tracks and compute the minimum ground track distance (i.e., x and y, but no z) between each hit and each runway threshold. The runway used by the track is the arg min di, where di is the distance to each runway threshold i. Step 3: Compute ROT for each track. ROT for a flight is defined as ROT = t1 - t0, where t0 is the �me the track crosses the runway threshold, and �me t1 is the �me the aircra� has exited the runway by more than x distance (e.g., 25 feet). One fast algorithm to determine when the aircra� track enters and then exits the polygon defining the runway boundary is to use a point-in-a-polygon method. This approach can be summarized as follows: Compare each side of the polygon to the Y (ver�cal) coordinate of the test point. Compile a list of nodes, where each node is a point where one side crosses the Y threshold of the test point. If there are an odd number of nodes on each side of the test point, then it is inside the polygon; if there are an even number of nodes on each side of the test point, then it is outside the polygon. Step 4: Collate ROT for ROT Distribu�on. A histogram can be created based on the ROT for each track for each runway. Histograms can also be created for each aircra� class or for each type of runway usage (e.g., high speed exit). An example histogram for ROT is shown in Figure B-4.

B-32 Evaluating Airfield Capacity B - 32 Figure B - 4. Example histogram for ROT. The best fit probability density function (pdf) for ROT is generally an Erlang distribution (e.g., 20.5 + Erlang [ 4.51,6 ] ). When outliers are removed from the data, a n ormal pdf provides the best fit (e.g., mean = 47.5, sigma = 11). Table B - 1 shows an example of ROT by aircraft type. Table B - 2 shows an example of ROT by r unway used. Table B - 1. Example ROT by aircraft type. Category Count Min imum Max imum Median Mean Standard Deviation Small 104 22 115 45 47.6 15.1 Large 1710 21 194 46 47.3 11.6 B757 140 24 82 48 48.5 9.5 Heavy 81 29 92 53 56.4 15.8 Table B - 2. Example ROT by aircraft type. Runway Count Min imum Max imum Median Mean Standard Deviation 18L/36R 21 30 76 52 51.4 9.5 18R/36L 765 22 92 42 43.4 9.1 17R/35L 29 40 139 54 58.2 18.3 17C/35C 859 28 194 48 48.6 10.7 17L/35R 27 32 76 47 50.1 10.2 13L/31R 1 59 59 59 59 0 13R/31L 333 21 115 54 54.4 16 0 50 100 150 200 0 50 100 150 200 250 300 350 400 Runway Occupancy Time (sec)

Abbreviations and acronyms used without definitions in TRB publications: AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S.DOT United States Department of Transportation