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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.

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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

Modeling and simulations of three future unmanned aircraft system (UAS) missions are described in this paper: air quality monitoring, wildfire monitoring, and on-demand air taxi. These missions are expected to have high benefit-to-cost ratio, and hence, a high potential for early adoption and integration into the national airspace system (NAS). Due to lack of historical data, input from subject matter experts involved in the air quality and wildfire monitoring domains was obtained to understand the challenges involved in modeling these two missions. On the other hand, the on-demand air taxi mission involved a strong socio-economic component, wherein the general public is directly involved in influencing the characteristics of the mission. Consequently, an activity-based modeling approach was adopted to model this mission. Demand data for the three missions were compiled into standard flight data sets (FDSs) that can be distributed to researchers across academia and the industry to conduct various impact studies on the integration of UAS into NAS. Since this research is part of an ongoing project, details of initial simulations and analyses conducted using these FDSs will also be described. NAS performance in the simulations was measured in terms of number of blind-encounter events and flight delays, before and after the introduction of UAS flights. Blindencounters are separation violations that occur in a hypothetical uncontrolled airspace.