Senanu Ashiabor

Logit Models for Forecasting Nationwide Intercity Travel Demand in the United States

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 Travel Survey. Separate models were developed for business and nonbusiness trip purposes. The explanatory variables used in the utility functions of the models were travel time, travel cost, and travelers household income. Given an input county-to-county trip demand table, the models were used to estimate county-to-county travel demand by automobile and commercial airline between all counties and commercial-service airports in the United States. The model has been integrated into a computer software framework called the transportation systems analysis model that estimates nationwide intercity travel demand in the United States.

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.

Integrated Model for Studying Small Aircraft Transportation System

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.

Nationwide Impacts of Very Light Jet Traffic in the Future Next Generation Air Transportation System (NGATS)

*† ‡ § ** †† This paper describes a methodology to predict on-demand air taxi services using emerging Very Light Jets (VLJ) technology in the future National Air Transportation System (NAS). The paper describes airspace and airport impacts of VLJ traffic considering an improved Next Generation Air Transportation System (NGATS). The analysis presented fits within the framework of the Transportation Systems Analysis Model (TSAM) developed by the Air Transportation Systems Laboratory at Virginia Tech for NASA Langley Research Center. 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).

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

Development of an Airport Choice Model for General Aviation Operations

The general aviation airport choice model is used to estimate general aviation (GA) person trips and number of aircraft operations through a set of airports given initial trip demand (GA person trips) from a set of counties. A pseudogravity model embedded in the model is used to distribute the intercounty person trips to the set of airports. The interairport person trips are then split into person trips by aircraft type (single, multiple, or jet engine). To split the trips, an attractiveness factor is developed on the basis of average occupancy, level of use, a distance distribution factor, and number of operations of each aircraft type. The person trips by aircraft type are then converted to aircraft operations with the use of occupancy factors for each aircraft type. The final model output is the number of aircraft operations by each aircraft type, in the form of three interairport trip tables. The estimated GA operations provide a means of assessing the impact of GA activities on the National Airspace System. The model output may also be used to assess the viability of GA aircraft as a competitive mode of transportation for intercity travel.

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

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.

The Effects of Projected Future Demand Including Very Light Jet Air-Taxi Operations on U.S. National Airspace System Delays as a Function of Next Generation Air Transportation System Airspace Capacity

This paper presents the results from a study which investigates the potential effects of the growth in air traffic demand including projected Very Light Jet (VLJ) air-taxi operations adding to delays experienced by commercial passenger air transportation in the year 2025. The geographic region studied is the contiguous United States (U.S.) of America, although international air traffic to and from the U.S. is included. The main focus of this paper is to determine how much air traffic growth, including VLJ air-taxi operations will add to enroute airspace congestion and determine what additional airspace capacity will be needed to accommodate the expected demand. Terminal airspace is not modeled and increased airport capacity is assumed.