AIDC project number: 309042
Ming Lee (UAF)
In the last few years, increasing scientific evidence supports the hypothesis that greenhouse gas emissions contribute to changes in the earth's climate, with many detrimental effects already taking place. There is developing consensus among the public and elected officials about the need for action to reduce GHG. This project will develop methodologies to forecast long-range multi-modal travel demand in urban areas that can reflect the effectiveness of strategies and policies designed to reduce vehicular-source GHG emissions. The developed methodologies will be tested with the two existing Metropolitan Planning Organization models available in Alaska: Anchorage Metropolitan Area Transportation Solutions and the Fairbanks Metropolitan Area Transportation System. In the USA, a major source of GHG emissions is carbon dioxide emissions from personal automobiles. The amount of CO2 emissions from mobile sources is tied directly to the amount of fuel consumed, which is then tied to the total vehicle miles traveled. In devising effective multi-modal transportation policies and financial programs for VMT reduction (i.e. reduction in CO2 emissions) in a metropolitan area, a travel-demand forecasting model with sufficient spatial and temporal resolution is needed to generate traffic forecasts. The forecasts will be used as inputs for air quality models such as the EPA's MOBILE6 or MOVES. MOVES, which will replace MOBILE6 as the next-generation mobile source emission model, requires traffic volume forecasts with spatial and temporal details that exceed what the current travel-demand models can produce.
