Impacts of cooperative adaptive cruise control on freeway traffic flow
Abstract
This study used microscopic simulation to estimate the effect on highway capacity of varying market penetrations of vehicles with adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC). Because the simulation used the distribution of time gap settings that drivers from the general public used in a real field experiment, this study was the first on the effects of ACC and CACC on traffic to be based on real data on driver usage of these types of controls. The results showed that the use of ACC was unlikely to change lane capacity significantly. However, CACC was able to increase capacity greatly after its market penetration reached moderate to high percentages. The capacity increase could be accelerated by equipping non-ACC vehicles with vehicle awareness devices so that they could serve as the lead vehicles for CACC vehicles.
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Driverless vehicles in communication with each other will be able to safely reduce the distance between vehicles because all members of the resulting convoy will be able to brake or accelerate simultaneously as required.
“Driverless vehicles in communication with each other will be able to safely reduce the distance between vehicles because all members of the resulting 'convoy' will be able to brake or accelerate simultaneously as required (Shladover et al., 2012; Fagnant and Kockelman, 2015a, p. 170; Gordon and Lidberg, 2015, p. 970).”
From When human beings are like drunk robots: Driverless vehicles, ethics, and the future of transport (2017) · cites this paper as Shladover et al., 2012
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Shi, X., & Li, X. S. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103134 · Cited by 111 · see citing works
Paradoxes of reservation-based intersection controls in traffic networks
Levin, M. W., Boyles, S. D., & Patel, R. (2016) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2016.05.013 · Cited by 110 · see citing works
Mahdinia, I., Arvin, R., Khattak, A. J., & Ghiasi, A. (2020) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198120918572 · Cited by 109 · see citing works
A control matching model predictive control approach to string stable vehicle platooning
Kianfar, R., Falcone, P., & Fredriksson, J. (2015) · Control Engineering Practice · doi:10.1016/j.conengprac.2015.09.011 · Cited by 107 · see citing works
Ge, J. I., & Orosz, G. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.07.021 · Cited by 107 · see citing works
Evaluation and modelling of the traffic flow effects of truck platooning
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- Authors
- Shladover, S. E., Su, D., & Lu, X.-Y.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Shladover, Steven E.
- Venue
- Transportation Research Record: Journal of the Transportation Research Board
- Publisher
- SAGE Publications
- Published
- 2012-01
- Volume
- 2324
- Issue
- 1
- Pages
- 63-70
- ISSN
- 0361-1981 (ISSN-L)
- 2169-4052
- OpenAlex
- W1991095535
- Cited by
- 887
- References
- 17
- Citations by year
- 2026: 53 · 2025: 78 · 2024: 77 · 2023: 84 · 2022: 98 · 2021: 118 · 2020: 109 · 2019: 85 · 2018: 70 · 2017: 50 · 2016: 31 · 2015: 23 · 2014: 6 · 2013: 4
- FWCI
- 15.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Record Journal of the Transportation Research Board (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Keywords
- cooperative adaptive cruise control, adaptive cruise control, freeway traffic flow, road capacity, market penetration, microscopic traffic simulation, lane capacity, driver behavior
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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