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Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies
HighlightsWe forecast U.S. adoption of connected and autonomous vehicle (CAV) technologies.Blind spot monitoring is the most attractive Level 1 technology for Americans.Average willingness to pay (WTP) for connectivity & full automation: $67 & $5857.Fleet simulations vary technology prices, WTP, and regulations over time.Predict 24% (pessimistic) to 87% (optimistic) Level 4 U.S. vehicle fleet by 2045.
AbstractAutomobile manufacturers, transportation researchers, and policymakers are interested in knowing the future of connected and autonomous vehicles (CAVs). To this end, this study proposes a new simulation-based fleet evolution framework to forecast Americans’ long-term (year 2015–2045) adoption levels of CAV technologies under eight different scenarios based on 5% and 10% annual drops in technology prices; 0%, 5%, and 10% annual increments in Americans’ willingness to pay (WTP); and changes in government regulations (e.g., mandatory adoption of connectivity on new vehicles). This simulation was calibrated with data obtained from a survey of 2167 Americans, regarding their preferences for CAV technologies (e.g., WTP) and their household’s annual vehicle transaction decisions.Long-term fleet evolution suggests that the privately held light-duty-vehicle fleet will have 24.8% Level 4 AV penetration by 2045 if one assumes an annual 5% price drop and constant WTP values (from 2015 forward). This share jumps to 87.2% if one uses a 10% annual rate of decline in prices and a 10% annual rise in WTP values. Overall, simulations suggest that, without a rise in most people’s WTP, or policies that promote or require technologies, or unusually rapid reductions in technology costs, it is unlikely that the U.S. light-duty vehicle fleet’s technology mix will be anywhere near homogeneous by the year 2045.
Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies
HighlightsWe forecast U.S. adoption of connected and autonomous vehicle (CAV) technologies.Blind spot monitoring is the most attractive Level 1 technology for Americans.Average willingness to pay (WTP) for connectivity & full automation: $67 & $5857.Fleet simulations vary technology prices, WTP, and regulations over time.Predict 24% (pessimistic) to 87% (optimistic) Level 4 U.S. vehicle fleet by 2045.
AbstractAutomobile manufacturers, transportation researchers, and policymakers are interested in knowing the future of connected and autonomous vehicles (CAVs). To this end, this study proposes a new simulation-based fleet evolution framework to forecast Americans’ long-term (year 2015–2045) adoption levels of CAV technologies under eight different scenarios based on 5% and 10% annual drops in technology prices; 0%, 5%, and 10% annual increments in Americans’ willingness to pay (WTP); and changes in government regulations (e.g., mandatory adoption of connectivity on new vehicles). This simulation was calibrated with data obtained from a survey of 2167 Americans, regarding their preferences for CAV technologies (e.g., WTP) and their household’s annual vehicle transaction decisions.Long-term fleet evolution suggests that the privately held light-duty-vehicle fleet will have 24.8% Level 4 AV penetration by 2045 if one assumes an annual 5% price drop and constant WTP values (from 2015 forward). This share jumps to 87.2% if one uses a 10% annual rate of decline in prices and a 10% annual rise in WTP values. Overall, simulations suggest that, without a rise in most people’s WTP, or policies that promote or require technologies, or unusually rapid reductions in technology costs, it is unlikely that the U.S. light-duty vehicle fleet’s technology mix will be anywhere near homogeneous by the year 2045.
Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies
Bansal, Prateek (author) / Kockelman, Kara M. (author)
Transportation Research Part A: Policy and Practice ; 95 ; 49-63
2016-10-16
15 pages
Article (Journal)
Electronic Resource
English
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