Skip to main content

Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas

Abstract

Shared autonomous (fully-automated) vehicles (SAVs) represent an emerging transportation mode for driverless and on-demand transport. Early actors include Google and Europe’s CityMobil2, who seek pilot deployments in low-speed settings. This work investigates SAVs’ potential for U.S. urban areas via multiple applications across the Austin, Texas, network. This work describes advances to existing agent- and network-based SAV simulations by enabling dynamic ride-sharing (DRS, which pools multiple travelers with similar origins, destinations and departure times in the same vehicle), optimizing fleet sizing, and anticipating profitability for operators in settings with no speed limitations on the vehicles and at adoption levels below 10 % of all personal trip-making in the region. Results suggest that DRS reduces average service times (wait times plus in-vehicle travel times) and travel costs for SAV users, even after accounting for extra passenger pick-ups, drop-offs and non-direct routings. While the base-case scenario (serving 56,324 person-trips per day, on average) suggest that a fleet of SAVs allowing for DRS may result in vehicle-miles traveled (VMT) that exceed person-trip miles demanded (due to anticipatory relocations of empty vehicles, between trip calls), it is possible to reduce overall VMT as trip-making intensity (SAV membership) rises and/or DRS users become more flexible in their trip timing and routing. Indeed, DRS appears critical to avoiding new congestion problems, since VMT may increase by over 8 % without any ride-sharing. Finally, these simulation results suggest that a private fleet operator paying $70,000 per new SAV could earn a 19 % annual (long-term) return on investment while offering SAV services at $1.00 per mile for a non-shared trip (which is less than a third of Austin’s average taxi cab fare).

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. 1.

    This SAV was used during 97 of the 24-hour day’s 288 5-minute intervals, or for 8 h and 5 minutes. It was also stationary for a portion of some of these 97 intervals, when travelers were dropped off early in the interval, but the SAV had not yet been assigned to another traveler.

  2. 2.

    Added VMT reflects extra (unoccupied) travel by SAVs, and reflects travel reductions due to DRS. Total added VMT is calculated by comparing the amount of travel in a given scenario to the amount of travel for the exact same population, if every person were driving a personal vehicle directly from his/her origin to his/her destination.

  3. 3.

    New shared-trips are the rise in the number of trips shared over the average simulated day, not whole new person-trips.

  4. 4.

    Boesler (2012) notes the U.S.’s top 27 selling vehicles sold for between $16,000 and $27,000. SAVs are assumed here to be relatively compact cars or mid-size cars, so a $20,000 base price assumption was made here.

  5. 5.

    Wait costs were excessive with a fleet of just 1500 SAVs, eliminating almost all profit in the base-case scenario.

References

  1. Agatz, N., Erera, A., Savelsbergh, M., Wang, X.: Dynamic Ride-Sharing: a simulation study in metro Atlanta. Transp. Res. Part B 45, 1450–1464 (2011)

    Article  Google Scholar 

  2. American Automobile Association.: Your driving costs: how much are you really Paying to Drive?, Heathrow (2012)

  3. Bell, M.G., Iida, Y.: Transportation Network Analysis. Wiley, New York (1997)

    Book  Google Scholar 

  4. Boesler, M.: The 27 Best Selling Vehicles in America. Business Insider (2012)

  5. Bureau of Labor Statistics (2014).: Metropolitan and Nonmetropolitan Area Occupational Employment and Wage Estimates May 2013: Austin-Round Rock-San Marcos, Washington, D.C (2014)

  6. Fagnant, D.J.: The future of fully automated vehicles: opportunities for vehicle- and ride-sharing with cost and emissions savings. Doctoral Dissertation in Civil Engineering. University of Texas at Austin, Austin (2014)

  7. Fagnant, D.J., Kockelman, K.: Environmental implications for autonomous Shared Vehicles, Using Agent-Based Model Scenarios. Transp. Res. Part C 40, 1–13 (2014a)

    Article  Google Scholar 

  8. Fagnant, D.J., Kockelman, K.: Development and Application of a Network-Based Shared Fully-Automated Vehicle Model in Austin, Texas. Presented at the 2014 Transportation Research Board Conference on Innovations in Travel Modeling, Baltimore, MD (2014b)

  9. Federal Highway Administration.: National Household Travel Survey. U.S. Department of Transportation, Washington, D.C. http://nhts.ornl.gov/index.shtml (2009)

  10. Jung, J., Jayakrishnan, R., Park, J.Y.: Design and modeling of real-time shared-taxi dispatch algorithms. Transportation Research Board 92nd Annual Meeting Compendium of Papers. Report 13–1798 (2013)

  11. Kornhauser, A., Chang A., Clark C., Gao J., Korac D., Lebowitz B., Swoboda A.: Uncongested Mobility for All: New Jersey’s Area-Wide Ataxi System. Princeton University. Princeton. http://orfe.princeton.edu/~alaink/NJ_aTaxiOrf467F12/ORF467F12aTaxiFinalReport_Draft.pdf (2013)

  12. Litman, T.: Transportation Cost and Benefit Analysis II—Travel Time Costs. Victoria Transport Policy Institute, Victoria. http://www.vtpi.org/tca/tca0502.pdf (2013)

  13. Maciejewski, M., Nagel, K.: Towards Multi-Agent Simulation of the Dynamic Vehicle Routing Problem in MATSim. PPAM 2011, Part II. Lecture Notes in Computer Science, vol. 7204, pp. 551–560. Springer, Heidelberg (2012)

  14. Markoff, J.: Google’s Next Phase in Driverless Cars: No Steering Wheel or Brake Pedals. New York Times (2014)

  15. MATSim: Multi-Agent Transport Simulation. Version 5.0. http://www.matsim.org (2013)

  16. Puget Sound Regional Council.: 2006 Household Activity Survey, Seattle (2006)

  17. Shao, R., Chang,L.: a new maximum power point tracking method for photovoltaic arrays using golden section search algorithm. Proceedings of the 2008 Canadian Conference on Electrical and Computer Engineering. Niagra Falls, (2008)

  18. Shoup, D.: Cruising for parking. Access 30, 16–22 (2007)

    Google Scholar 

  19. Stevens, M., Marans,B.: Toronto hybrid taxi pilot. Toronto Atmospheric Fund. Toronto. http://www.fleetwise.ca/taxi.pdf (2009)

  20. TaxiFareFinder.com.: Taxi Fare Finder–Austin (2014)

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Kara M. Kockelman.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fagnant, D.J., Kockelman, K.M. Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation 45, 143–158 (2018). https://doi.org/10.1007/s11116-016-9729-z

Download citation

Keywords

  • Connected and autonomous vehicles
  • Shared autonomous vehicles
  • MATSim simulation
  • Dynamic ride-sharing