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Passenger cars in the United States continue to incorporate increasing levels of technology and features. However, deployment of technology requires substantial development and time in the automotive sector. Prior analyses indicate that deployment of technology in the automotive sector can be described by a logistic function. These analyses refer to maximum annual growth rates as high as 17% and with developmental times of 10-15 years. However, these technologies vary widely in complexity and function, and span decades in their implementation. This work applies regression with a logistic form to a wide variety of automotive features and technologies and, using secondary regression, identifies broader trends across categories and over time.

Transport policy research seeks to predict and substantially reduce the future transport-related greenhouse gas emissions and fuel consumption to prevent negative climate change impacts and protect the environment. However, making such predictions is made difficult due to the uncertainties associated with the anticipated developments of the technology and fuel situation in road transportation, which determine the total fuel use and emissions of the future light-duty vehicle fleet. These include uncertainties in the performance of future vehicles, fuels' emissions, availability of alternative fuels, demand, as well as market deployment of new technologies and fuels. This paper develops a methodology that quantifies the impact of uncertainty on the U.S. transport-related fuel use and emissions by introducing a stochastic technology and fleet assessment model that takes detailed technological and demand inputs.