On the Impact of Cooperative Autonomous Vehicles in Improving Freeway Merging: A Modified Intelligent Driver Model-Based Approach
Abstract
Transport researchers and practitioners have long been seeking capable solutions to deal with the traffic oscillations caused by freeway merging. Although existing approaches based on ramp metering have improved the overall efficiency of on-ramps, their performance is still far below the theoretical capacity. The recently proposed detecting technology of autonomous vehicles (AVs) provides an alternative for maximizing the merging efficiency by developing and using appropriate controllers for AVs. In this paper, we develop a cooperative intelligent driver model in order to examine the system performance under different proportions of AVs. The results show that, with a proper vehicle-to-vehicle controlling mechanism, an increasing percentage of AVs will reduce the total travel time and smooth traffic oscillations.
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Cited by
Sorted by citations
Milakis, D., van Arem, B., & van Wee, B. (2017) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2017.1291351 · Cited by 963 · see citing works
Faisal, A., Yigitcanlar, T., Kamruzzaman, M., & Currie, G. (2019) · Journal of Transport and Land Use · doi:10.5198/jtlu.2019.1405 · Cited by 427 · see citing works
“In a merging situation, if different ratios of AVs equipped with longitudinal and lateral detecting technology, and advance cruise control (ACC) are penetrated on freeway with human driven vehicles, cooperative intelligent driver model (CIDM) of AVs could practically improve the freeway performance (Xiao & Gao, 2010;…”
BackgroundMethodInfluentialNetwork traffic instability with automated driving and cooperative merging
Gu, Z., Wang, Z., Liu, Z., & Saberi, M. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103626 · Cited by 57 · see citing works
- Authors
- Zhou, M., Qu, X., & Jin, S.
- Affiliations
- Griffith University (AU)
- University of Technology Sydney (AU)
- Zhejiang University (CN)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2016
- Pages
- 1-7
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2526634643
- Cited by
- 291
- References
- 0
- Citations by year
- 2026: 6 · 2025: 28 · 2024: 36 · 2023: 28 · 2022: 32 · 2021: 38 · 2020: 52 · 2019: 38 · 2018: 18 · 2017: 15
- FWCI
- 19.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Griffith Research Online (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- freeway merging, Intelligent Driver Model, vehicle-to-vehicle communication, traffic oscillations, travel time reduction, traffic flow capacity
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