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Climbing Mount Next: The Effects of Autonomous Vehicles on Society

Author

Listed:
  • David Levinson

    (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

Abstract

The United States spent almost the entire twentieth century climbing Mount Auto. From the 1920s onward, the automobile was the dominant mode of travel for Americans, accumulating more miles per capita than other modes. While the Great Depression slowed the auto’s growth, it did not result in decline. There was a brief downturn during World War II, and a few hiccups in the steady rise of mileage. But the later 2000s and 2010s have seen a sharp downturn in motor vehicle use per capita. This drop is greater than the drop during World War II in absolute terms (though the War saw a drop of twenty-three percent off the pre-war peak, and the 2012 drop is seven percent below 2005). It is complemented by an apparent plateauing in total miles of paved roads since 2008. Within the transportation sector there have been small shifts over the past fifteen years, which cannot explain much of the decline of travel. There are active transportation modes, like walking and biking, which work well for short trips, and certainly have niches they can grow into if land development intensifies and people reorganize their lives to enable them. For instance, I am one of the seven percent of Minneapolitans who walk to work. The numbers are much lower outside core cities, and nationally, at three percent. Transit ridership per capita is up ever so slightly. There are a slew of new mobility options which use information technologies to allow travel without owning an automobile, but are not yet visible in the transportation statistics. These include peer-to-peer taxi and ridesharing services and dynamic real-time rental cars. While these are useful in their niches, they likely are not cost-effective enough to be the main transportation mode for the vast majority of the population with the given technology. Today these new mobility options are supplements when the main mode does not solve the job to be done. In the future, that might change. Technologies allow people to do more of the same, and they allow people to do new things. It is easier to predict more of the same than new things.

Suggested Citation

  • David Levinson, 2015. "Climbing Mount Next: The Effects of Autonomous Vehicles on Society," Working Papers 000139, University of Minnesota: Nexus Research Group.
  • Handle: RePEc:nex:wpaper:mountnext
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    File URL: http://hdl.handle.net/11299/180054
    File Function: First version, 2015
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    Citations

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    Cited by:

    1. Long T. Truong & Chris Gruyter & Graham Currie & Alexa Delbosc, 2017. "Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria, Australia," Transportation, Springer, vol. 44(6), pages 1279-1292, November.
    2. Christian Ulrich & Benjamin Frieske & Stephan A. Schmid & Horst E. Friedrich, 2022. "Monitoring and Forecasting of Key Functions and Technologies for Automated Driving," Forecasting, MDPI, vol. 4(2), pages 1-24, May.
    3. Millard-Ball, Adam, 2019. "The autonomous vehicle parking problem," Transport Policy, Elsevier, vol. 75(C), pages 99-108.
    4. Tin Pofuk, 2017. "Autonomous Vehicles and the Future of Private Vehicle Ownership," MIC 2017: Managing the Global Economy; Proceedings of the Joint International Conference, Monastier di Treviso, Italy, 24–27 May 2017,, University of Primorska Press.
    5. Tin Pofuk, 2018. "Autonomous Vehicles: What Will the Future Look Like?," MIC 2018: Managing Global Diversities; Proceedings of the Joint International Conference, Bled, Slovenia, 30 May–2 June 2018,, University of Primorska Press.
    6. Marletto, Gerardo, 2019. "Who will drive the transition to self-driving? A socio-technical analysis of the future impact of automated vehicles," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 221-234.

    More about this item

    Keywords

    GPS data; non-work trips; land use; axis of travel; destination choice;
    All these keywords.

    JEL classification:

    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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