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dc.contributor.authorBaik, Hojong
dc.contributor.authorTrani, Antonio A.
dc.contributor.authorHinze, Nicolas
dc.contributor.authorSwingle, Howard
dc.contributor.authorAshiabor, Senanu
dc.contributor.authorSeshadri, Anand
dc.date.accessioned2017-11-02T13:44:08Z
dc.date.available2017-11-02T13:44:08Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/10919/79916
dc.description.abstractA nationwide model predicts the annual county-to-county person roundtrips 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.en_US
dc.description.sponsorshipThe National Consortium for Air Mobility, NASA, and the Joint Program and Development Office provided financial support to the Air Transportation Systems Laboratory at Virginia Polytechnic Institute and State University to develop the model.en_US
dc.language.isoen_USen_US
dc.publisherTransportation Research Board of the National Academiesen_US
dc.titleForecasting Model for Air Taxi, Commercial Airline, and Automobile Demand in the United Statesen_US
dc.typeArticleen_US
dc.title.serialTransportation Research Record: Journal of the Transportation Research Boarden_US
dc.identifier.doihttps://doi.org/10.3141/2052-02
dc.identifier.volume2052en_US


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