Automated vehicles (AVs) may enter the consumer market with various stages of automation in 10 years or even sooner. Meanwhile, regional planning agencies are envisioning plans for time horizons out to 2040 and beyond. To help decision makers understand the effect of AV technology on regional plans, modeling tools should anticipate its impact on transportation networks and traveler choices. This research uses the Seattle, Washington, region's activity-based travel model to test a range of travel behavior impacts from AV technology development. The existing model was not originally designed with AVs in mind, so some modifications to the model assumptions are described in areas of roadway capacity, user values of time, and parking costs. Larger structural model changes were not yet considered. Results of four scenario tests show that improvements in roadway capacity and in the quality of the driving trip may lead to large increases in vehicle miles traveled, while a shift to per mile usage charges may counteract that trend. Travel models will need to have major improvements in the coming years, especially with regard to shared ride, taxi modes, and the effect of multitasking opportunities, to better anticipate the arrival of this technology.

Program and general revenues from the State of Texas. AbstractThe built environment can be used to influence travel demand, but very few studies consider the relative energy savings of such policies in context of a complex urban system. This analysis quantifies the day-to-day and embodied energy consumption of four different neighborhoods in Austin, Texas, to examine how built environment variations influence various sources of urban energy consumption. A microsimulation combines models for petroleum use (from driving) and residential and commercial power and natural gas use with rigorously measured building stock and infrastructure materials quantities (to arrive at embodied energy). Results indicate that the more suburban neighborhoods, with mostly detached single-family homes, consume up to 320% more embodied energy, 150% more operational energy, and about 160% more total life-cycle energy (per capita) than a densely developed neighborhood with mostly low-rise-apartments and duplexes. Across all neighborhoods, operational energy use comprised 83 to 92% of total energy use, and transportation sources (including personal vehicles and transit, plus street, parking structure, and sidewalk infrastructure) made up 44 to 47% of the life-cycle energy demands tallied. Energy elasticity calculations across the neighborhoods suggest that increased population density and reduced residential unit size offer greatest life cycle energy savings per capita, by reducing both operational demands from driving and home energy use, and from less embodied energy from construction. The results support the notion that transportation and the built environment are strongly linked, and improving urban energy efficiency must come from policies and designs targeting embodied sources, not just a household's travel and daily energy consumption. While much research has considered built environment (BE) impacts on travel choices, much less research has considered impacts on buildings and infrastructure, even though buildings consume nearly 2.5 times the energy used for U.S. personal transport. Furthermore, the embodied energy of materials for constructing and maintaining buildings and other infrastructure is rarely considered alongside purported transportation energy savings from different BE designs. Thus, a more holistic energy analysis is typically overlooked, and various sectors of the urban environment (e.g., vehicles and roads, residential and commercial buildings) are too rarely compared to identify the most effective "levers" for reducing energy consumption. This approach evaluates the life-cycle energy demands of existing and theoretical neighborhoods in Austin, Texas, in a way that explicitly identifies key levers for urban energy reduction. For instance, how much total energy can be saved by increasing a given neighborhood density, and in which sectors (transportation, buildings, infrastructure) will those impacts be most critical? This analysis emphasizes a more holistic evaluation of BE variations, to better evaluate relative ene...

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