Nicolas Hinze

Forecasting Model for Air Taxi, Commercial Airline, and Automobile Demand in the United States

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.

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

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

Utilizing Traveler Demand Modeling to Predict Future Commercial Flight Schedules in the NAS

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

Unmanned aircraft system demand generation and airspace performance impact prediction

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. Blind-encounters are separation violations that occur in a hypothetical uncontrolled airspace.

UAS Demand Generation Using Subject Matter Expert Interviews and Socio-economic Analysis

Two approaches employed in developing traffic demand estimates for nineteen civilian and commercial applications (“missions”) of unmanned aircraft systems (UAS) are described in this paper. The missions were sourced from RTCA’s DO-320 report and selected using two criteria: 1) UAS flights are operated within continental US (CONUS), and 2) the flights present the maximum potential for interaction with commercial airline traffic in CONUS. The first approach used input from subject matter experts (SMEs) via email and phone interviews to gather information on the state-of-the-art in conducting the missions, operationg costs, constraints involved, and regulatory, operational and economic challenges pertaining to the use of UAS. Information obtained from survey of scholarly literature and other publicly available data sources was used in conjunction with SME input. The second approach involved socio-economic analysis using the Transportation System Analysis Model (TSAM), and relied on socioeconomic data such as population census, demographic distribution and income distribution. The paper also presents a Java-based interactive computer tool to help users develop tailored traffic data from the demand estimates, using criteria such as geographic area of operation of UAS flights, cruise altitude, cruise speed and flight duration.

Development of a Intercity Mode Choice Models for New Aviation Technologies

A family of nested logit random utility models was developed to study intercity mode choice behavior in the United States. The models were calibrated using a nationwide revealed preference survey (1995 American Travel Survey) and two stated preference surveys conducted by Virginia Tech at selected airports in the U.S. The focus of this paper is on the ability of the models to estimate market share for the new category of Very Light Jet aircraft used in on-demand air taxi services. Analysis was performed to compare the stated preference surveys and the American Travel Survey within the same random utility framework. The main explanatory variables in the utility functions are travel time and travel cost stratified by household income. The model has been integrated into a large-scale computer software travel demand framework called the Transportation Systems Analysis Model to estimate nationwide intercity travel demand flow between 3,091 counties in the U.S., 443 commercial service airports and more than 3,000 general aviation airports in the U.S. A pared down version of the model will be integrated into the National Strategy Simulator that the FAA uses for strategic level planning the aviation system.

Integrating Air Transportation System Demand Predictions in Preliminary Aircraft Design

This paper describes a methodology to integrate air transportation demand estimates in the preliminary aircraft design process. The paper describes the adaptation of the Transportation Systems Analysis Model (TSAM) developed by the Air Transportation Systems Laboratory at Virginia Tech for NASA Langley Research Center to predict potential demand of aerospace vehicle concepts. TSAM uses traditional air transportation systems engineering techniques to: 1) predict the number of intercity trips generated in the country based on socio-economic factors, 2) distribute these trips across the country, 3) predict the most likely modes of transportation used to execute these trips, 4) predict flights and trajectories associated with air transportation trips, and 5) predict impacts of the intercity trips generated in the National Airspace System (NAS). The paper includes a case study to estimate the potential demand for advanced tilt-rotor aircraft technology operating in the Northeast Corridor in the United States. I.Introduction Traditional aircraft design analysis requires clear aircraft mission requirements and estimates of the number of vehicles to be produced in the program’s life cycle. Mission requirements are traditionally setup by the aircraft design team in consultation with the customer (typically airlines for commercial vehicle development). The determination of the potential market for the vehicle to be designed is more challenging to define. Airlines and aircraft manufacturers continuously revise their estimates of vehicle demand based on historical market outlooks. This uncertainty is perhaps best epitomized in the current battle between Airbus and Boeing about the potential demand for long-range commercial transport aircraft. Airbus justifies the design of very-large capacity aircraft such as the A380 on the grounds of mature origin-destination market consolidation and capacity constraints at existing hub airports. Boeing justifies the development of the 787 and 777-200LR aircraft on the grounds of market fragmentation across long-haul markets. This illustrates that existing techniques to predict market demand for new aerospace vehicles is a difficult task requiring an understanding of the interactions between social and technological factors. Very few models seem to exist to predict the potential demand of novel aerospace technologies. This is the main trust of this effort. NASA’s Systems Analysis Branch (SAB) is responsible for the evaluation of new aerospace vehicle concepts and thus has a vested interest at improving the modeling capabilities to predict vehicle demand.