IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0314044.html
   My bibliography  Save this article

A speed optimization model for connected and autonomous vehicles at expressway tunnel entrance under mixed traffic environment

Author

Listed:
  • Jianrong Cai
  • Yang Liu
  • Zhixue Li

Abstract

Rear-end collisions frequently occurred in the entrance zone of expressway tunnel, necessitating enhanced traffic safety through speed guidance. However, existing speed optimization models mainly focus on urban signal-controlled intersections or expressway weaving zones, neglecting research on speed optimization in expressway tunnel entrances. This paper addresses this gap by proposing a framework for a speed guidance model in the entrance zone of expressway tunnels under a mixed traffic environment, comprising both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs). Firstly, a CAV speed optimization model is established based on a shooting heuristic algorithm. The model targets the minimization of the weighted sum of the speed difference between adjacent vehicles and the time taken to reach the tunnel entrance. The model’s constraints incorporate safe following distances, speed, and acceleration limits. For HVs, speed trajectories are determined using the Intelligent Driver Model (IDM). The CAV speed optimization model, represented as a mixed-integer nonlinear optimization problem, is solved using A Mathematical Programming Language (AMPL) and the BONMIN solver. Safety performance is evaluated using Time-to-Collision (TTC) and speed standard deviation (SD) metrics. Case study results show a significant decrease in SD as the CAV penetration rate increases, with a 58.38% reduction from 0% to 100%. The impact on SD and mean TTC is most pronounced when the CAV penetration rate is between 0% and 40%, compared to rates above 40%. The minimum TTC values at different CAV penetration rates consistently exceed the safety threshold TTC*, confirming the effectiveness of the proposed control method in enhanced safety. Sensitivity analysis further supports these findings.

Suggested Citation

  • Jianrong Cai & Yang Liu & Zhixue Li, 2024. "A speed optimization model for connected and autonomous vehicles at expressway tunnel entrance under mixed traffic environment," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-21, December.
  • Handle: RePEc:plo:pone00:0314044
    DOI: 10.1371/journal.pone.0314044
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0314044
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0314044&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0314044?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yao, Zhihong & Wang, Yi & Liu, Bo & Zhao, Bin & Jiang, Yangsheng, 2021. "Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway," Energy, Elsevier, vol. 230(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lifeng Wang & Hu Liang & Yuxin Jian & Qiang Luo & Xiaoxiang Gong & Yiwei Zhang, 2024. "Optimized path planning and scheduling strategies for connected and automated vehicles at single-lane roundabouts," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-20, August.
    2. Salvini, Pericle & Kunze, Lars & Jirotka, Marina, 2024. "On self-driving cars and its (broken?) promises. A case study analysis of the German Act on Autonomous Driving," Technology in Society, Elsevier, vol. 78(C).
    3. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    4. Junyan Han & Xiaoyuan Wang & Gang Wang, 2022. "Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review," Sustainability, MDPI, vol. 14(13), pages 1-27, July.
    5. He, Yongming & Kang, Jia & Pei, Yulong & Ran, Bin & Song, Yuting, 2021. "Research on influencing factors of fuel consumption on superhighway based on DEMATEL-ISM model," Energy Policy, Elsevier, vol. 158(C).
    6. Kumar, Gokula Manikandan Senthil & Guo, Xinman & Zhou, Shijie & Luo, Haojie & Wu, Qi & Liu, Yulin & Dou, Zhenyu & Pan, Kai & Xu, Yang & Yang, Hongxing & Cao, Sunliang, 2025. "State-of-the-art review of smart energy management systems for supporting zero-emission electric vehicles with X2V and V2X interactions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    7. Kwangho Ko & Tongwon Lee & Seunghyun Jeong, 2021. "A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data," Sustainability, MDPI, vol. 13(20), pages 1-15, October.
    8. Dong, Haoxuan & Shi, Junzhe & Zhuang, Weichao & Li, Zhaojian & Song, Ziyou, 2025. "Analyzing the impact of mixed vehicle platoon formations on vehicle energy and traffic efficiencies," Applied Energy, Elsevier, vol. 377(PA).
    9. Yang, Yichen & Li, Zuxing & Li, Yabin & Cao, Tianyu & Li, Zhipeng, 2023. "Stability enhancement for traffic flow via self–stabilizing control strategy in the presence of packet loss," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    10. Ma, Guangyi & Li, Keping, 2024. "Modeling impacts of different data transmission delays on traffic jam, fuel consumption and emissions on curved road," Energy, Elsevier, vol. 310(C).
    11. Weiqiang Zhou & Haoxu Guo & Lihao Yao, 2023. "Statistical Modeling of Traffic Flow in Commercial Clusters Based on a Street Network," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    12. Zhaoming Zhou & Jianbo Yuan & Shengmin Zhou & Qiong Long & Jianrong Cai & Lei Zhang, 2023. "Modeling and Analysis of Driving Behaviour for Heterogeneous Traffic Flow Considering Market Penetration under Capacity Constraints," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    13. Li, Jianqi & Yang, Hang & Cheng, Rongjun & Zheng, Pengjun & Wu, Bing, 2024. "A dynamic temporal and spatial speed control strategy for partially connected automated vehicles at a signalized arterial," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    14. Qi, Weiwei & Zou, Zhenyu & Ruan, Lianjie & Wu, Jiabin, 2024. "Method for designing fuel-efficient highway longitudinal slopes for intelligent vehicles in eco-driving scenarios," Applied Energy, Elsevier, vol. 368(C).
    15. Yao, Zhihong & Gu, Qiufan & Jiang, Yangsheng & Ran, Bin, 2022. "Fundamental diagram and stability of mixed traffic flow considering platoon size and intensity of connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    16. Chen, Shuiwang & Hu, Lu & Yao, Zhihong & Zhu, Juanxiu & Zhao, Bin & Jiang, Yangsheng, 2022. "Efficient and environmentally friendly operation of intermittent dedicated lanes for connected autonomous vehicles in mixed traffic environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    17. Jiang, Chenming & Yin, Shicong & Yao, Zhihong & He, Junliang & Jiang, Rui & Jiang, Yu, 2024. "Safety evaluation of mixed traffic flow with truck platoons equipped with (cooperative) adaptive cruise control, stochastic human-driven cars and trucks on port freeways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    18. Jiang, Yangsheng & Wang, Sichen & Yao, Zhihong & Zhao, Bin & Wang, Yi, 2021. "A cellular automata model for mixed traffic flow considering the driving behavior of connected automated vehicle platoons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    19. Li, Yun & Zhang, Wenshan & Zhang, Shengrui & Pan, Yingjiu & Zhou, Bei & Jiao, Shuaiyang & Wang, Jianpo, 2024. "An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    20. Jiang, Yangsheng & Cong, Hongwei & Chen, Hongyu & Wu, Yunxia & Yao, Zhihong, 2024. "Adaptive cruise control design for collision risk avoidance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0314044. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.