UVM Transportation Center funds research for new signature project
Release Date: 09-24-2007
Researchers at the UVM College of Engineering and Mathematical Sciences (CEMS) have partnered with the Vermont Law School (VLS) on a Signature Project to create a complex systems model to simulate the transportation energy market. The project, entitled "Multi-Scale Model of the U.S. Transportation Energy Market for Policy Assessment," is funded by the UVM Transportation Center (UTC) in the amount of $330,000 direct over two years. The UTC will provide another $170,000 in indirect funds and CEMS and VLS will provide matching funds of $330,000, making this an $830,000 project.
The resulting model will serve as a valuable decision support tool for policy makers in determining how to influence the market in desirable directions and help move the country towards a sustainable transportation system.
CEMS researchers Margaret Eppstein, Jeff Marshall and Donna Rizzo will work with Michael Dworkin, director of the VLS Environmental Law Program, the premier environmental law program in the nation. They will lead multidisciplinary teams of CEMS graduate and undergraduate students and VLS students in developing a multi-scale agent-based model for assessing the impact of energy policy on the alternative energy transportation market.
Achievement of federal targets for alternative energy usage will require large-scale infrastructure investment in new types of vehicles; in facilities for extraction, refining, and transportation of different alternative fuels; in biofuel feedstock production and transportation facilities; in fuel storage facilities; in fuel supply stations; and in research and research facilities necessary to provide technological advances required for alternative fuels to be feasible. Actions and incentives by federal, state and local governments will play a critical role in influencing the transportation energy market.
The proposed approach
The researchers will develop a multi-scale agent-based complex systems model intended to be used for assessment of different policy tools for optimization of alternative energy usage and infrastructure investment. Two scales of modeling will be considered a city scale, in which the actions of agents represent choices made by individual users, and a national scale, in which agents represent the aggregate population of a town or city. An additional set of corporate agents will be used to represent corporations responsible for infrastructure development, including automobile manufacturers, national fuel suppliers and local fueling station owners.
An innovative approach using artificial neural networks will be developed to connect these two scales. The resulting model will help policy makers to predict how the U.S. market might respond to various possible regulatory actions such as governmental mandates or taxes/rebates on fuel or vehicles aimed at consumers, vehicle manufacturers, fuel producers and suppliers in order to help them craft effective regulatory strategies that promote sustainable transportation.