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The current transportation system is generally regarded as unsustainable. The significant contribution of the transport sector to the degradation of urban air quality, threat of climate change, dependence on nonrenewable resources, and negative societal effects all lead to this conclusion. Strategies that have been proposed to try to solve the transportation problem include Intelligent Transportation Systems (ITS) as well as economic and other incentives for more sustainable travel modes, vehicles, and patterns. Unfortunately, the most promising strategies involve losses of the comfort, convenience, and personalized service benefits currently provided by passenger cars. This paper presents a technology-based concept for a sustainable passenger transportation system of the future. The building block of the system is a small, electrically propelled pod. We call this the Personal Automobile Unit (PAU). By itself, the PAU would be suitable for local trips on low-speed roadways.

A principal motivation for introducing alternative fuels is to reduce air pollution and greenhouse gas emissions. A comprehensive evaluation of the reductions must include all Life Cycle activities from the vehicle operation to the feedstock extraction. This paper focuses on the fuel upstream activities only. We compare the results and methods of the three most comprehensive existing fuel upstream models in the U.S.A. and we explore the differences and uncertainties of these types of analyses. To explicitly include the impact of uncertainties, we create a new model using the following approaches: Instead of using a single value as input, the new model deals with ranges around the most probable value. Ranges are discussed and calibrated by an expert network, in terms of their relative probability. Probabilistic function techniques are applied to study the impact of the uncertainties on the model output.