Aviation demand forecast models with unknown structure . 14

Different organizations have developed their own proprietary demand forecast models.

The details of these proprietary models are not well known. But some of them holds authoritative status and their numbers are widely used in aviation researches. The most well known among them are the forecasts made by Boeing, Airbus, and Federal Aviation Association (FAA).

The Boeing Company annually publishes “Current Market Outlook” which con-tains information about economic issues and air travel growth for world travel (The Boeing Company, 2008). It projects air travel demand and the fleets of aircrafts

required to meet the demand. It has separate projection by international regions, represented by North America, Latin America, Europe, Africa, Middle East, North-east Asia, SouthNorth-east Asia, Southwest Asia, China, and Oceania. It provides gross level information about travel demand and is not appropriate for obtaining informa-tion on demand or operainforma-tions at specific airports in the CONUS. The most recent one is “Current Market Outlook 2008-2027”, which shows how air transport will change over the next 20 years.

Airbus also has an equivalent 20 year forecast named “Global Market Forecast”

for the international air travel demand and the fleets of aircraft needed to meet the anticipated demand (Airbus S.A.S., 2007). It also provides gross level information about travel demand and is not appropriate for obtaining information on demand or operations at specific airports in the CONUS. The most recent one at the moment is

“Global Market Forecast 2007 — 2026”.

FAA has a couple of demand forecast models, among which the Terminal Area Forecast (TAF) system is the official forecast of aviation activity at FAA facilities.

The TAF provides forecasts for active airports in the National Plan of Integrated Airport System (NPIAS). The TAF includes forecasts for FAA towered airports, federally contracted towered airports, nonfederal towered airports, and non-towered airports. Detailed forecasts are prepared for large air carriers, air taxi/commuters, general aviation, and military. An internet server of FAA provides the historical data and forecasts, which can be queried using any web browser (Federal Aviation Administration, 2009b). FAA updates TAF annually and constantly improves the forecast method. The TAF provides forecasts of the following flight activities.

• Enplanements for air carrier and commuter

• Aircraft operations including

– Itinerant operations for air carrier, air taxi/commuter, general aviation,

and military

– Local operations (takeoff and landing at the same airport) for general aviation and military

• Instrument operations

TAF summary report is also available to the general public (Federal Aviation Ad-ministration, 2008b). More information on TAF database is provided in Appendix D.

2.1.2 Aviation demand forecast models with known structure

Aviation demand models in this category have publicly available publications on their processes and structures. In this section, three representative demand-centric models with known structure are overviewed , namely the AvDemand model, the Transportation Systems Analysis Model (TSAM), and the Mi model.

AvDemand is a software tool that calculates future NAS demand based on FAA forecasts. It was developed by Sensis Corporation for use by National Aeronautics and Space Administration (NASA) to generate future air transportation demand in Air Traffic Management (ATM) experiments (Huang et al., 2004). It has spatially-explicit representation of the CONUS. The outputs have a generic format so that it can be integrated with other NASA simulation tools. It does not include intermodal and multimodal relationships and it does not capture consumer behavior. AvDe-mand is composed of three main functions, namely deAvDe-mand generation, deAvDe-mand data input/output, and demand analysis. Demand generation is the core component of AvDemand and offers two approaches: a flight-based demand growth approach and a passenger-weighted demand growth approach. Figure 8 shows the demand genera-tion features of AvDemand. In the flight-based demand growth approach, AvDemand starts with the baseline flight demand set and assumes a growth rate to generate tar-get future demand sets. In the passenger-weighted demand generation approach, AvDemand also starts with the baseline flight demand set and estimates passenger

demand from the flight demand. It then applies passenger growth rates to calculate passenger demand for the target future demand. Flight schedules and flight plans are calculated using a fleet-mix determination and departure time distribution algorithm.

Figure 8: AvDemand demand generation features. [Source: Huang and Schleicher (2007)]

TSAM is a database-driven demand prediction model to estimate long distance travel at a county level based upon population and demographics in multimodal scope (Viken et al., 2006a; Trani et al., 2003; Baik and Trani, 2005). It is initially developed by Virginia Tech to study Small Aircraft Transportation System (SATS) program. The mode choice is performed based on the trip purpose, trip cost, time and time value of the trip. TSAM adops a nested multinomial logit model for mode choice.

The county level demand for air travel after the mode choice is then aggregated to the airport level. The airport level O-D demand is then assigned to specific routes follow-ing the traditional four-step transportation plannfollow-ing framework (More information

on the traditional four-step process can be found in Appendix. A). Figure 9 shows the model framework of TSAM. When future flight demand growth is modeled, TSAM utilizes baseline flight schedules and uses the FRATAR algorithm to develop the fu-ture schedules in the NAS (More information on FRATAR algorithm can be found in Appendix. A.3.3). These projected flights can be plugged into air transportation simulators to analyze the impact of the projected demand on the NAS. Viken et al.

(2006a) used the Airspace Concepts Evaluation System (ACES), which was developed under NASA’s Virtual Airspace Modeling and Simulation (VAMS) project (Couluris et al., 2003; Zelinski, 2005; National Aeronautics and Space Administration, 2004), as the airspace simulation program for his research. A description of some of the widely known airspace simulation programs is provided in Appendix B

Figure 9: TSAM model structure. [Source: Viken et al. (2006a)]

Mi is an agent-based model developed by Lewe at Georgia Tech (Lewe, 2005;

Lewe et al., 2006). Both TSAM and Mi forecast travel demand at the National Transportation System (NTS) level and consider intermodal and multimodal aspects.

However Mi is not spatially-explicit but uses virtual NTS concept, where agents reside and perform transportation activities. The CONUS in Mi is represented with four locales, which are large-, medium-, small-, and non-metropolitan areas. Using Agent-Based Modeling (ABM), it tries to capture behavioral aspects of travelers. Its entity-centric abstraction makes the simulation less computationally complicated. It models transportation consumers and service providers as agents. Transportation consumers are modeled based on the demographic and economic characteristics of the locales. Transportation service providers generate price and time information for each mode of transportation and business model. Then consumer agents perform transportation mode choice using a multinomial logit model. Figure 10 shows flow charts of the Mi model. “TAF” in the graph stands for Transportation Architecture Field while “S/Ps” represents service providers. The result from the simulation is annual-based and calibration was performed against the 1995 ATS data.