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Forecasting the Impact of Connected and Automated Vehicles on Energy Use A Microeconomic Study of Induced Travel and Energy Rebound

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Document pages: 39 pages

Abstract: Connected and automated vehicles (CAVs) are expected to yield significantimprovements in safety, energy efficiency, and time utilization. However, theirnet effect on energy and environmental outcomes is unclear. Higher fuel economyreduces the energy required per mile of travel, but it also reduces the fuelcost of travel, incentivizing more travel and causing an energy "reboundeffect. " Moreover, CAVs are predicted to vastly reduce the time cost of travel,inducing further increases in travel and energy use. In this paper, we forecastthe induced travel and rebound from CAVs using data on existing travelbehavior. We develop a microeconomic model of vehicle miles traveled (VMT)choice under income and time constraints; then we use it to estimateelasticities of VMT demand with respect to fuel and time costs, with fuel costdata from the 2017 United States National Household Travel Survey (NHTS) andwage-derived predictions of travel time cost. Our central estimate of thecombined price elasticity of VMT demand is -0.4, which differs substantiallyfrom previous estimates. We also find evidence that wealthier households havemore elastic demand, and that households at all income levels are moresensitive to time costs than to fuel costs. We use our estimated elasticitiesto simulate VMT and energy use impacts of full, private CAV adoption under arange of possible changes to the fuel and time costs of travel. We forecast a2-47 increase in travel demand for an average household. Our results indicatethat backfire - i.e., a net rise in energy use - is a possibility, especiallyin higher income groups. This presents a stiff challenge to policy goals forreductions in not only energy use but also traffic congestion and local andglobal air pollution, as CAV use increases.

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