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Capacity of a freeway lane with platoons of autonomous vehicles mixed with regular traffic

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  • Sala, Marcel
  • Soriguera, Francesc

Abstract

In the near future, autonomous vehicles (AVs) will travel sharing the current freeways with human driven vehicles. The efficiency of this mixed traffic scenario will depend on the ability of AVs to behave cooperatively. Otherwise, the introduction of uncoordinated AVs might lead to capacity reductions. Connected AVs (CAVs) traveling in platoons represents a promising management strategy to get the most from the AVs’ technological revolution. Most of previous research has used traffic microsimulation tools to assess platooning and other CAVs’ cooperative driving strategies, achieving good results. However, the robust macroscopic modeling alternative, which typically yields the necessary insights and fundamental knowledge to set the foundations for the development of management and control strategies, remains almost unexplored. This paper contributes to fill this research gap by providing a generalized macroscopic model to estimate the average CAVs platoon length for a given traffic demand and penetration rate of CAVs. Two different platooning schemes are compared (i.e. cooperative and opportunistic) representing the best and worst case scenarios. The estimation of CAVs platoon length is of much importance as it is the main factor driving capacity improvements on freeways, which under the appropriate conditions could exceed 10.000 vehicles per hour and lane.

Suggested Citation

  • Sala, Marcel & Soriguera, Francesc, 2021. "Capacity of a freeway lane with platoons of autonomous vehicles mixed with regular traffic," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 116-131.
  • Handle: RePEc:eee:transb:v:147:y:2021:i:c:p:116-131
    DOI: 10.1016/j.trb.2021.03.010
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    Cited by:

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    2. Cong-Jian Liu & Fang-Kai Wang & Zhuang-Zhuang Wang & Tao Wang & Ze-Hao Jiang, 2022. "Autonomous Vehicles for Enhancing Expressway Capacity: A Dynamic Perspective," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    3. Guo, Mengting & Bai, Yang & Li, Xia & Zhou, Wei & Wang, Chunyang & Ma, Xinwei & Gao, Huixin & Xiao, Yuewen, 2023. "Freeway capacity modeling and analysis for traffic mixed with human-driven and connected automated vehicles considering driver behavior characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    4. Guan, Hao & Wang, Hua & Meng, Qiang & Mak, Chin Long, 2023. "Markov chain-based traffic analysis on platooning effect among mixed semi- and fully-autonomous vehicles in a freeway lane," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 176-202.
    5. Chen, Shukai & Wang, Hua & Xiao, Ling & Meng, Qiang, 2022. "Random capacity for a single lane with mixed autonomous and human-driven vehicles: Bounds, mean gaps and probability distributions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    6. Margarita Martínez-Díaz & Maximilià-Miquel Montes Carbó, 2024. "Assessing User Acceptance of Automated Vehicles as a Precondition for Their Contribution to a More Sustainable Mobility," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    7. Li, Haijian & Zhang, Junjie & Sun, Xiaoliang & Niu, Jun & Zhao, Xiaohua, 2022. "A survey of vehicle group behaviors simulation under a connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    8. Li, Pengbo & Tian, Lijun & Xiao, Feng & Zhu, Hongwei, 2022. "Can day-to-day dynamic model be solved analytically? New insights on portraying equilibrium and accommodating autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 374-395.

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