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Journal ArticleDOI

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

Daniel J Fagnant, +1 more
- 01 Jan 2018 - 
- Vol. 45, Iss: 1, pp 143-158
TLDR
In this article, the authors investigate the potential of shared autonomous vehicles (SAVs) for U.S. urban areas via multiple applications across the Austin, Texas, network and show 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.
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).

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References
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Journal ArticleDOI

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TL;DR: In this article, the authors describe the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use.
Journal ArticleDOI

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TL;DR: In this article, the authors present a model of how drivers choose whether to cruise or to pay, and it predicts several results: drivers are more likely to cruise if curb parking is cheap, off-street parking is expensive, fuel is cheap and they want to park for a long time, and they place a low value on saving time.
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Transportation network analysis

TL;DR: This book presents a coherent approach to the analysis of transportation networks based on the concept of network equilibrium and the application of convex programming methods, and indicates promising areas for further research.

Transportation network analysis

Mgh Bell, +1 more
TL;DR: In this article, the authors present a coherent approach to the analysis of transportation networks based on the concept of network equilibrium and the application of convex programming methods, and provide an introduction to the terminology used in the book.
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Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta

TL;DR: In this article, the problem of matching drivers and riders in a dynamic setting is considered, and optimization-based approaches are developed to minimize the total systemwide vehicle miles incurred by system users, and their individual travel costs.
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