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


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).

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  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.


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Correspondence to Kara M. Kockelman.

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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).

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