Preparing Peak Period and Operational Profiles—Guidebook (2013)

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64 This chapter provides techniques for estimating existing and future day/night splits and stage length profiles for use in the INM or the forthcoming AEDT. Also included is a discussion of the factors that may cause the balance between day and night operations to change, along with guidance on avoiding common pitfalls. Estimates of existing and future day/night splits of aircraft operations by equipment type are required for noise analysis using the INM (see Chapter 6 for noise analysis using the NIRS model). These analyses require the division of aircraft operations into daytime flights (7 AM to 10 PM) and nighttime flights (10 PM to 7 AM). In California, an evening segment (7 PM to 10 PM) is also required. Noise impacts are a function of aircraft type and the payload they carry. Although load factors will vary, the required fuel load will be dependent on the length of the flight. When fuel loads are high, the aircraft is heavier, and it can gain altitude less quickly. To compensate, the aircraft must run on higher power to meet climb requirements, and therefore generates more noise. Consequently, estimates of aircraft stage length that organize aircraft departures into increments of 500 nautical miles are required. This chapter first presents guidance on how to compile data on existing activity into a format suitable for the INM model. Next, guidance on preparing forecast day/night and stage length profiles is presented, followed by general comments and cautions. 7.1 INM Input for Existing Conditions Exhibit 7.1 shows an approach for estimating an existing day/night fleet mix and involves the following steps: 1. Collect existing annual aircraft operations data by aircraft type and time of flight, and also by destination for aircraft departures. Appendix B provides a list of data sources. Generally the best source of data, if available, is Air Traffic Control Tower (ATCT) radar collected on behalf of the airport, since this data covers all operations including general aviation (GA) and provides actual time of arrival and departure rather than scheduled time. USDOT T-100 data can provide information on fleet mix and stage length for commercial passenger and cargo carriers, but cannot provide time of day information. Since noise studies are intended to be representative of the entire year, data should be collected for an entire consecutive 12 month period if possible. The OAG can provide scheduled passenger operations data by airline, aircraft type, time of day, and stage length, but should be used with caution. The times reflect gate times rather than runway times, and do not account for delay. This data source therefore tends to underestimate the nighttime percentage of passenger operations. See Appendix I in the ACRP WOD 14 for additional discussion. C h a p t e r 7 Preparation of Day/Night and Stage Length Profiles for Noise Analysis

preparation of Day/Night and Stage Length profiles for Noise analysis 65 In most cases some manipulation will be required to convert the data into the proper format for noise analysis, specifically: a. Eliminate or correct typographic errors in the radar data. In some instances the intended aircraft type is clear; in those instances where the aircraft type is not clear it should be reclassified as an unknown. b. Scale up identified flights to match annual totals. You will need to do this to offset missing information or information in which the aircraft type is identified as “unknown.” This is best done separately by category (passenger carrier, cargo, GA, etc.) because some categories, such as military, tend to account for a disproportionate number of “unknown” flights. c. Identify the airline for commercial carriers. This is important because often airlines may fly the same aircraft type, but still differ in the type of engine used, which in turn differ in noise characteristics. The engine-use information is not available from OAG, T-100, or radar data. d. In some cases, such as general aviation operations operating under Visual flight rules (VFR), no operations data will be available. Estimates can be obtained from knowledgeable on-airport individuals such as fixed base operators (FBO). Based aircraft information can be used as a proxy if no other data on GA is available. However, it should be done with caution since the mix of transient operations does not always match the mix of based aircraft operations. 2. Designate flights as day or night based on the time of day information and the required definition of day and night, or evening if applicable. Exhibit 7.1. Preparation of INM input for existing conditions. Operations by Aircraft Type, Destination, and Time of Day (Step 1) Screen, Refine and Sort Data (Step 2) Organize Arrivals and Departures by Day and Night (Step 3) Organize Departures by Stage Length (Step 4) Convert to Average Annual Day Operations (Step 5) Assign Runway Use and Flight Tracks (Step 6) INM Input - Day/Night Split & Stage Length (Step 7) User Definition of Day/Night Split Data Input User Determined Assumptions Intermediate Output Final Output

66 preparing peak period and Operational profiles—Guidebook 3. Categorize flights by stage length based on the distance to destinations. The stage length categories used by INM are as follows: a. 0 to 500 nautical miles b. 501 to 1000 nautical miles c. 1001 to 1500 nautical miles d. 1501 to 2500 nautical miles e. 2501 to 3500 nautical miles f. 3501 to 4500 nautical miles g. 4501 to 5500 nautical miles h. 5501 to 6500 nautical miles i. 6501 and more nautical miles 4. Convert annual operations into AAD operations by dividing by the number of days in the year analyzed. 5. Assign flight tracks and runway use based on radar data and/or discussion with ATCT. 6. Code the operations for use in the INM or upcoming AEDT model. The INM model has specific categories of aircraft that do not always correspond to the categories in the radar data. In addition, some very new aircraft types are not modeled in the INM. In those instances, substitutes must be identified and approved by the FAA. It is unlikely that any data set will be complete and totally accurate with respect to the needs of noise analysis. In those instances, additional research will be required on the part of the user to pre- pare defensible assumptions to fill in the data gaps. Some assumptions will have more of an impact on the results than others, which should help to determine priorities in preparing these assumptions. 7.2 Future INM Fleet Mix INM inputs representing future conditions require a fleet mix forecast. A fleet mix forecast is best prepared as part of the annual forecast, since the fleet mix will determine average aircraft size (measured in seats per aircraft). Average aircraft size, along with average load factor, determines passengers per passenger aircraft operation, and that in turn allows a forecast of passenger aircraft operations to be derived from a forecast of passenger enplanements. Ideally, therefore, the fleet and aircraft operations forecast will be prepared at the same time to ensure consistency. Fleet mix forecasts require some judgment on the part of the practitioner. Important consid- erations include: • Aircraft orders by the carriers serving the airport. • Announced plans on aircraft retirements. • Anticipated new markets, including size and distance. Example 7.1. Scaling aircraft operations. The total number of GA operations for which the aircraft type can be identified from the radar data is 46,000. The known total of GA operations is 51,000. The ratio of the two numbers is (51,000/46,000) or 1.1087. Multiply the operations for each identified aircraft type by 1.1087. The sum of the results will equal 51,000.

preparation of Day/Night and Stage Length profiles for Noise analysis 67 • Airfield capabilities, i.e., runway length. • Level of competition—highly competitive markets tend to generate more frequencies with smaller aircraft than similarly sized non-competitive markets. Exhibit 7.2 shows an approach for estimating the INM future day/night fleet mix at an airport which involves the following steps: 1. Select an annual fleet mix forecast and choose the year of analysis. 2. Obtain existing day/night stage length profile. If none are available, use the approach in Exhibit 7.1 to prepare an existing profile. 3. Organize the forecast of operations by category (passenger, cargo, GA, etc.). 4. Determine whether a change in the overall day/night split in each category is warranted. See Appendix I in ACRP WOD 14 for additional guidance. 5. Estimate the future day/night split for each aircraft type in each category. Typically this is done by assuming the same day/night split that currently applies to that aircraft type. This may cause a distortion in the overall distribution, however. When a new aircraft type replaces an existing aircraft type, it will take over the existing aircraft’s mission and therefore acquire its day/night distribution characteristics. Some adjustment may therefore be required to ensure that the aggregate nighttime percentage for the category is achieved. Example 7.2 is an illustration of this. Exhibit 7.2. Preparation of INM input for future conditions. User Definition of Day/Night Split Existing Day/Night Split of Operations (Step 2) Organize Operations by Category (Step 3) • Passenger • Cargo, etc. Convert to Average Annual Day Operations (Step 7) INM Input–Future Conditions (Step 9) Prepare Day/Night Split Targets by Category (Step 4) Adjust Day/Night and Stage Length by Aircraft Type to Match Category Targets (Steps 5 and 6) Forecast of Annual Operations by Aircraft Type (Step 1) Increase/Decrease in Percent of Night Operations Change in Stage Length Distribution Assign Runway Use and Flight Tracks (Step 8) Existing Runway Use and Flight Tracks User Defined Changes in Runway Use and Flight Tracks Data Input User Determined Assumptions Intermediate Output Final Output

68 preparing peak period and Operational profiles—Guidebook Example 7.2. Adjusting nighttime fleet mix to match control total. Assume a simple case with three aircraft types showing the base year fleet mix below: Annual Departures - Forecast With Adjustment Day Night Total Night (%) Boeing 737 - 800 43,891 6,109 50,000 12.2 Boeing 737 - 700 70,226 9,774 80,000 12.2 MD - 80 15,113 4,887 20,000 24.4 Total 129,231 20,769 150,000 13.8 Day Night Total Night (%) Boeing 737-800 27,000 3,000 30,000 10.0 Boeing 737-700 45,000 5,000 50,000 10.0 MD-80 40,000 10,000 50,000 20.0 Total 112,000 18,000 130,000 13.8 Annual Departures - Existing Day Night Total Night (%) Boeing 737-800 45,000 5,000 50,000 10.0 Boeing 737-700 72,000 8,000 80,000 10.0 MD-80 16,000 4,000 20,000 20.0 Total 133,000 17,000 150,000 11.3 Annual Departures - Forecast Without Adjustment Ratio of Future Unadjusted to Existing Night % 13.8% divided by 11.3% = 1.22 Assume an annual forecast that provides the fleet mix projections in green. If the base year nighttime percentages are applied to each aircraft type, the total nighttime percentage would fall from 13.8 percent to 11.3 percent. This is because the aircraft types that are growing (Boeing 737-700 and 737-800) have lower than average nighttime percentages in this case. Multiply the nighttime percentage for each aircraft type by the ratio (1.22) to generate adjusted nighttime percentages (in orange) for each aircraft type. Calculate adjusted nighttime departures (in blue) by multiplying total departures by the adjusted nighttime percentage. Daytime departures are equal to total departures less nighttime departures. To adjust for this, calculate the ratio of the new unadjusted nighttime percentage (11.3 percent) to the base year nighttime percentage (13.8 percent). Note that the adjusted total forecast nighttime percentage (13.8 percent) is now the same as the base year nighttime percentage.

preparation of Day/Night and Stage Length profiles for Noise analysis 69 6. Estimate the future stage length for each aircraft type in each category. It may not be safe to assume that the future stage length distribution for an aircraft type will be the same as the existing distribution. Airlines often dedicate their newer aircraft to long-haul routes and older aircraft to short-haul routes since the fuel economies associated with new aircraft are better realized on longer routes. However, as these new aircraft take over routes from existing aircraft they will gradually assume some of their stage length characteristics as well. Therefore, some adjustment may be necessary to ensure that the aggregate stage length distributions for the category are achieved. 7. Divide the results from Steps 5 and 6 by the number of days in the year to produce a future AAD day/night fleet mix. 8. Assign future runway use and flight tracks. Incorporate any anticipated changes in air traffic control practices and airspace structure. Typically information on Standard Terminal Arrival Routes (STARs) and Standard Instrument Departure (SID) routes, along with radar data such as NOMS or PDARS, is used to generate model input for existing flight tracks and runway use distributions. These distributions can be modified to represent future conditions by using output from airfield/airspace simulation models or input from ATCT staff. 9. Code the operations for use in the INM or upcoming AEDT model. 7.3 Comments and Cautions Consider the following when preparing a future day/night fleet mix: • In general, long-haul travel has been growing faster than short-haul travel, and this may affect the overall stage length distributions. This is likely to be especially important at constrained airports, where airlines are more likely to preserve the more financially remunerative long-haul flights at the expense of short-haul flights. In addition, potential future competing transportation modes, such as high-speed rail, are more likely to compete with short-haul and medium-haul air travel than with long-haul travel. • International passenger flights warrant special attention. Historically, most overseas flights from U.S. airports have been to Europe and Japan. These flights tend to operate almost exclu- sively during the day due to the time zones and connecting banks at these airports. However, these daytime distributions do not necessarily apply to some of the newer overseas markets. For example, most flights to southern South America are red-eye flights which often land and take-off during the night at U.S. airports. Flights to South Asia (India and surrounding countries) and Oceania (Australia/New Zealand) also often land and take-off at night (see Appendix N in ACRP 03-12 Final Report). Therefore, it is essential to pay special attention to shifts in inter- national service. For obvious reasons, these shifts will also affect stage length distributions.