{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/87204"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/87204","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Alternative approaches to forecasting highway related revenues in Virginia","abstract":"The highway related revenues for the Commonwealth of Virginia from three major tax sources; fuel tax, registration fee, and sales and use tax are estimated under three scenarios. Each scenario assumes different economic conditions for the future. The base case expects normal or moderate situations for future economy, where the optimistic case expects lower inflation rates and the pessimistic case assumes higher inflation rates. Two modeling approaches have been used in forecasting the fuel tax revenue. One is based on travel, and the other is based on gasoline demand. The sales and use tax revenue has also been forecasted using two different approaches. One method depends on the demand for vehicle, and the other on the historical amount of revenues generated. Registration fee revenue for five types of vehicles are forecasted using number of registered vehicles and the average registration fees. A comparison of the developed model with other existing state revenue forecasting models are also presented.","abstract_html":"The highway related revenues for the Commonwealth of Virginia from three major tax sources; fuel tax, registration fee, and sales and use tax are estimated under three scenarios. Each scenario assumes different economic conditions for the future. The base case expects normal or moderate situations for future economy, where the optimistic case expects lower inflation rates and the pessimistic case assumes higher inflation rates. Two modeling approaches have been used in forecasting the fuel tax revenue. One is based on travel, and the other is based on gasoline demand. The sales and use tax revenue has also been forecasted using two different approaches. One method depends on the demand for vehicle, and the other on the historical amount of revenues generated. Registration fee revenue for five types of vehicles are forecasted using number of registered vehicles and the average registration fees. A comparison of the developed model with other existing state revenue forecasting models are also presented.","abstract_has_math":false,"creators":["Jamei, Bahram"],"institution":"Virginia Polytechnic Institute and State University","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Civil Engineering","degree_department":"Civil Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1982,"date_issued":"1982","date_published":"1982","updated_at":"2026-07-22T22:18:39Z","subjects":[],"languages":["en_US"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10919/87204","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Civil Engineering"]},{"key":"dc:creator","label":"Author","values":["Jamei, Bahram"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-01-31T18:27:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-01-31T18:27:15Z"]},{"key":"dc:date.issued","label":"Date","values":["1982"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Polytechnic Institute and State University"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/87204"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The highway related revenues for the Commonwealth of Virginia from three major tax sources; fuel tax, registration fee, and sales and use tax are estimated under three scenarios. Each scenario assumes different economic conditions for the future. The base case expects normal or moderate situations for future economy, where the optimistic case expects lower inflation rates and the pessimistic case assumes higher inflation rates. Two modeling approaches have been used in forecasting the fuel tax revenue. One is based on travel, and the other is based on gasoline demand. The sales and use tax revenue has also been forecasted using two different approaches. One method depends on the demand for vehicle, and the other on the historical amount of revenues generated. Registration fee revenue for five types of vehicles are forecasted using number of registered vehicles and the average registration fees. 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The base case expects normal or moderate situations for future economy, where the optimistic case expects lower inflation rates and the pessimistic case assumes higher inflation rates. Two modeling approaches have been used in forecasting the fuel tax revenue. One is based on travel, and the other is based on gasoline demand. The sales and use tax revenue has also been forecasted using two different approaches. One method depends on the demand for vehicle, and the other on the historical amount of revenues generated. Registration fee revenue for five types of vehicles are forecasted using number of registered vehicles and the average registration fees. 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