Speaker: Dr. Thomas F. Golob, Institute of Transportation Studies University of California, Irvine

A challenging problem in transportation planning is how to forecast demand for new products and services that might be so different from what exists so as to invalidate existing data of consumer behavior. An example is the demand for cars and light-duty trucks powered by electricity and other "clean" fuels such as compressed natural gas (CNG) and methanol. California emission regulations mandate that, by 2003, at least 25% of the new car fleet must be low emission vehicles. These vehicles are likely to differ from future conventional-fuel vehicles not only in terms of fuel costs and vehicle price, but more importantly in non-monetary attributes such as availability of fuel, the range before refueling or recharging, performance and emissions reduction.

Research Conducted in 1991 for the California Energy Commission uses a survey in which hypothetical choices are constructed according to an experimental design, and probabilistic choice models are used to measure attribute importance. This approach merges the market research method typically known as conjoint measurement with discrete-choice travel demand modeling. This survey was administered to about 7000 residents in Southern California. Results are interesting enough to brag about, and, where possible, compare well with results from car-type choice models based on actual market data. An intriguing question is: will consumers adopt a "green" attitude and trade off additional monetary or inconvenience cost for reduced vehicle emissions, or will they just hope their neighbors