Cost, Energy, and Environmental Impact of Automated Electric Taxi Fleets in Manhattan
Open accessAbstract
Shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50-90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be $0.29-$0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and $0.05-$0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.
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Unless there is a dramatic drop in oil prices or conventional car prices, electric vehicles will become cheaper.
“Bauer et al. (2018) state that, unless there is a dramatic drop in oil prices or conventional car prices, 575 electric vehicles will become cheaper.”
From Shared autonomous vehicle services: A comprehensive review (2020) · cites this paper as Bauer et al., 2018
In “Comparison with conventional taxi fleets,” the paper finds that a battery-electric taxi fleet would be cheaper than a comparable conventional fleet unless both fuel prices and conventional vehicle purchase prices fall dramatically. The citing sentence broadens this fleet-specific finding to electric vehicles generally and changes “both” price drops to an apparent “or” condition. The printed “575” appears to be a page-number artifact, not part of the claim. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThrough trip-chaining, autonomous taxis could radically reduce the number of vehicles required, potentially at the cost of increased vehicle turn-over and longer distances.
“Through trip-chaining, autonomous taxis (ATs) could radically reduce the number of vehicles required, potentially at the cost of increased vehicle turn-over and longer distances.”
From Material efficiency strategies to reducing greenhouse gas emissions associated with buildings, vehicles, and electronics—a review (2019) · cites this paper as [136]
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Self-citation
- Authors
- Bauer, G. S., Greenblatt, J. B., & Gerke, B. F.
- Affiliations
- University of California, Berkeley (US)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Bauer, Gordon S.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-03-28
- Volume
- 52
- Issue
- 8
- Pages
- 4920-4928
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W2795059094
- Cited by
- 195
- References
- 29
- Citations by year
- 2026: 14 · 2025: 20 · 2024: 27 · 2023: 29 · 2022: 25 · 2021: 32 · 2020: 22 · 2019: 19 · 2018: 7
- FWCI
- 7.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Environmental Science & Technology (publishedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- University of California Berkeley
- Lawrence Berkeley National Laboratory (DE-AC02-05CH11231)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- shared autonomous electric vehicles, battery range, charging infrastructure, service costs, greenhouse gas emissions, energy consumption, urban transport, Manhattan, agent-based modeling, taxi trip data, sensitivity analysis
- MeSH terms
- Air Pollution, Vehicle Emissions, Automobiles, New York City, Motor Vehicles
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