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Planning for Automated Vehicle Technologies Impact on Energy

Tyler C. Folsom, PhD, PE Project Manager, Qi2, Kent, WA Professor, University of Washington, Bothell, WA Tyler@TFolsom.com. Planning for Automated Vehicle Technologies Impact on Energy. Easy predictions:. Safer roads

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Planning for Automated Vehicle Technologies Impact on Energy

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  1. Tyler C. Folsom, PhD, PE Project Manager, Qi2, Kent, WA Professor, University of Washington, Bothell, WA Tyler@TFolsom.com Planning for Automated Vehicle TechnologiesImpact on Energy

  2. Easy predictions: • Safer roads • Self-driving taxis blur the distinction between public and private transportation • Less need for parking

  3. Four Futures 1. Cars with improved Driver Assistance Systems (DAS) but still requiring a driver. 2. Cars that typically drive themselves; no license required. 3. Transit based vehicle automation. 4. People moving from fixed homes to automated Recreational Vehicles. (requires #2)

  4. Why do big cars appeal? Be able to haul peak load and passengers Safety Comfort Status

  5. Automation supports smaller vehicles Base two-person pods can connect to form larger vehicles. The vehicle can be right-sized for the task at hand. If traffic accidents are rare, a motorcycle is almost as safe as an SUV. Small vehicles need not be cramped or Spartan.

  6. Ultra-light PRT+ • Reduced car ownership • Motorcycles that are almost as safe as an SUV • Post-automotive cities • Energy efficiency of 1000 (one thousand) mpg equivalent [1] • Urban transportation based on renewable electricity • Increased highway capacity with no new construction [2] • Public transportation more convenient, faster, safer and cheaper than private transportation

  7. Typical urban car average speeds

  8. Typical light rail average speeds Typical light rail average speeds

  9. Power to move a land vehicle Power = K1 * m * v + K2 * v3 Rolling + Aerodynamic m: mass; v: velocity The less-simplified version needs additional rolling power to overcome slopes or stop-and-go [9].

  10. Ultra-light Electric Transit Predictions Oncethereisalargepoolofdepletedbatteries,energycanbeharvestedanytimethesunshinesorthewindblows. Majorreductioninfossilfuelconsumption. Lessairandwaterpollution;improvedpublichealth. Fewergreen-housegasses. Oilisnolongerastrategiccommodity,andmilitaryspendingcandecrease.

  11. Possible Planning Implications • Less required parking. A few remote lots, rather than at each building. • Human-scaled communities? • Increased / decreased sprawl? • Parking for vehicular homeless? • Distributed solar & wind power generation? • Reduced need for heavy buses / trucks → lighter roads?

  12. Sources [1] Tyler C. Folsom, Energy and Autonomous Vehicles, IEEE Technology and Society Magazine, Summer 2012, draft on www.qi2.com/index.php/transportation [2] S. E. Shladover, “Reasons for operating AHS vehicles in platoons”, in Automated Highway Systems, P.A. Ioannou, Ed, New York, NY, Springer, 1996. [3] Federal Highway Administration (2009) Summary of Travel Trends – 2009 National Household Travel Survey. [4] S. C. Davis, S. W. Diegel and R. G. Boundy, Transportation Energy Data Book: Edition 29. July 2010 table 4.32. online cta.ornl.gov/data [5] http://articles.timesofindia.indiatimes.com/2012-01-18/mumbai/30638447_1_kmph-coastal-road-travel-speed [6] City of Yakima, Travel Speed Study of Urban Streets Using GPS and GIS, 2002. http://www.yakimawa.gov/services/gis/files/2012/05/reportman.pdf [7] Matt Johnson, Average scheduled speed: How does Metro compare? 2012. http://greatergreaterwashington.org/post/5183/average-schedule-speed-how-does-metro-compare/ [8] http://www.soundtransit.org/Rider-Guide/Link-light-rail [9] F.R. Whitt and D.G. Wilson, Bicycling Science, 2nded, Cambridge, MA: MIT Press 1982.

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