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Automated Vehicles, On-Demand Mobility, and Environmental Impacts

We review the history, current developments, projected future trends and environmental impacts of automated vehicles (AVs) and on-demand mobility, and explore potential synergies. Many automobile manufacturers and Google plan to release AVs between 2017 and 2020, with potential benefits including increased safety, more efficient road use, increased driver productivity and energy savings. Estimates of AV energy use and greenhouse gas (GHG) emissions range from an ~80 % or greater decrease to a threefold increase; however, we argue that net decreases are likely. On-demand mobility services exist in many cities around the world, with advances in mobile technology increasing their popularity. On-demand mobility can provide numerous transportation, land use, and environmental and social benefits, and users tend to decrease both vehicle ownership and annual vehicle distances traveled. Combining on-demand mobility and AVs may amplify adoption of both, and further lower energy use and GHG emissions through the use of small, efficient shared AVs.
- Lawrence Berkeley National Laboratory United States
- Lawrence Berkeley National Laboratory United States
- University of California System United States
- University of California, Berkeley United States
690, On-demand mobility, Greenhouse gas, Driverless, Energy use, Automated vehicles, Self-driving
690, On-demand mobility, Greenhouse gas, Driverless, Energy use, Automated vehicles, Self-driving
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).313 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 0.1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
