There are 3,091 counties in TSAM serving as the zones of travel activity in the continental United States. The trip-generation output is made up of two 3091 vectors: one for attractions and the other for productions for each county. Trip distribution fills up the cells between the vectors, creating a person-trip interchange table of demand between the two counties. Mode choice splits the demand between each county by mode of transportation. The mode choice model in TSAM and this paper estimates both the demand by mode between counties and the demand flows in the airport network associated with the counties. This is achieved by embedding an airport choice model in the mode choice model. Hence the model is both a mode choice and a partial trip assignment model. The framework for the process is shown in Figure 1. The modes of transportation considered in the TSAM model are commercial airline, automobile, SATS, and train. However, the focus in this paper is on the baseline model, which has only automobile and commercial airline modes. The trip assignment in TSAM involves converting the airport-to-airport person trips into aircraft operations, generating flights by using a time-of-day profile, and loading the flights on the National Airspace System to estimate the impact of aircraft operations in the system. The complete travel demand model is fully documented elsewhere (1-3).NASA is using TSAM to forecast future airport demands and assist the Joint Program Development Office (JPDO) in planning the next-generation air transportation system. NASA is also using TSAM to study demand for supersonic aircraft, tilt rotors, and short take-off and landing aircraft. This shows that the model is relevant and the output is critical to policy makers. This paper presents a family of logit models that have been developed since the SATS program to estimate intercity travel demand in the United States. LITERATURE REVIEW Review of Disaggregate Nationwide Travel Demand ModelsBetween 1976 and 1990, four major attempts were made to develop disaggregate national-level intercity mode choice models in the United States. All the models used versions of National Travel Surveys (NTS) conducted by the Bureau of the Census and the Bureau of Transportation Statistics (BTS). The first was a multinomial logit model by Stopher and Prashker in 1976, which used the 1972 NTS (4). Grayson developed a multinomial logit model by using the 1977 version of the NTS (5). Morrison and Winston were the first to apply a nested logit model (6). They used the log-sum variable to hierarchically nest three models: decision to rent a car, destination choice, and mode choice. Later, Koppelman extended Morrison's approach to Nested and mixed logit models were developed to study national-level intercity transportation in the United States. The models were used to estimate the market share of automobile and commercial air transportation of 3,091 counties and 443 commercial service airports in the United States. Models were calibrated with the use of the 1995 American Tra...

A systems engineering methodology was used to study the National Aeronautics and Space Administration’s (NASA’s) Small Aircraft Transportation System (SATS) concept as a feasible mode of transportation. The proposed approach employs a multistep intercity transportation planning process executed inside a Systems Dynamics model. Doing so permits a better understanding of SATS impacts to society over time. The approach is viewed as an extension to traditional intercity transport models through the introduction of explicit demand–supply causal links of the proposed SATS over the complete life cycle of the program. The modeling framework discussed is currently being used by the Virginia SATS Alliance to quantify possible impacts of the SATS program for NASA’s Langley Research Center. There is discussion of some of the modeling efforts carried out so far and of some of the transportation modeling challenges facing the SATS program ahead.

A nationwide model predicts the annual county-to-county person round-trips for air taxi, commercial airline, and automobile at 1-year intervals through 2030. The transportation systems analysis model (TSAM) uses the four-step transportation systems modeling process to calculate trip generation, trip distribution, and mode choice for each county origin-destination pair. Network assignment is formulated for commercial airline and air taxi demand. TSAM classifies trip rates by trip purpose, household income group, and type of metropolitan statistical area from which the round-trip started. A graphical user interface with geographic information systems capability is included in the model. Potential applications of the model are nationwide impact studies of transportation policies and technologies, such as those envisioned with the introduction of extensive air taxi service using very light jets, the next-generation air transportation system, and the introduction of new aerospace technologies.

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler's purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized

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