Multiagent Spatial Simulation of Autonomous Taxis for Urban Commute: Travel Economics and Environmental Impacts
Abstract
With the likelihood of autonomous vehicle technologies in public transport and taxi systems increasing, their impact on commuting in real-world road networks is insufficiently studied. In this study, an agent-based model is developed to simulate how commuters travel by autonomous taxis (aTaxis) in real-world road networks. The model evaluates the travel costs and environmental implications of substituting conventional personal vehicle travel with aTaxi travel. The proposed model is applied to the city of Ann Arbor, Michigan, to demonstrate the effectiveness of aTaxis. The results indicate that to meet daily commute demand with wait times less than 3 min, the optimized autonomous taxi fleet size is only 20% of the conventional solo-commuting personal car fleet. Commuting cost decreases by 38%, and daily vehicle utilization increases from 14 to 92 min When using internal combustion engine aTaxis, energy consumption, greenhouse gas (GHG) emissions, and SO2 emissions are respectively 16, 25, and 10% higher than conventional solo commuting, mainly because of unoccupied repositioning between trips. Given the emission intensity of the local electricity grid, the environmental impacts of electric aTaxis do not show significant improvement over conventional vehicles.
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Using autonomous shared vehicles alone could increase total vehicle-miles traveled and do not necessarily lead to lower emissions if vehicles have internal combustion engines or dirty electricity.
“However, using autonomous shared vehicles alone could increase the total vehicle-miles traveled and therefore do not necessarily lead to lower emissions as long as the vehicles have internal combustion engines (or electrical engines on a "dirty" electrical grid) [131,495].”
From Tackling climate change with machine learning (2023) · cites this paper as [495]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
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- Authors
- Lu, M., Taiebat, M., Xu, M., & Hsu, S.-C.
- Affiliations
- Hong Kong Polytechnic University (HK)
- University of Michigan (US)
- Corresponding author
- Hsu, Shu-Chien
- Venue
- Journal of Urban Planning and Development
- Published
- 2018-12
- Volume
- 144
- Issue
- 4
- ISSN
- 0733-9488 (ISSN-L)
- 1943-5444
- OpenAlex
- W2885711151
- Cited by
- 62
- References
- 46
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 6 · 2023: 6 · 2022: 11 · 2021: 8 · 2020: 14 · 2019: 9 · 2018: 1
- FWCI
- 4.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Urban Planning and Development (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Social Sciences › Transportation › Transportation Planning and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- autonomous taxis, autonomous vehicles, urban commuting, travel cost, vehicle fleet size, vehicle utilization, greenhouse gas emissions, sulfur dioxide emissions, energy consumption, electric vehicles, vehicle repositioning, agent-based modeling, Ann Arbor
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