IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13562-d948156.html
   My bibliography  Save this article

Multi-State Car-Following Behavior Simulation in a Mixed Traffic Flow for ICVs and MDVs

Author

Listed:
  • Chengju Song

    (Transportation Collage, Jilin University, Changchun 130022, China)

  • Hongfei Jia

    (Transportation Collage, Jilin University, Changchun 130022, China)

Abstract

With the development of intelligent connected vehicles (ICVs) and communication technology, collaborative operation among vehicles will become the trend of the future. Thus, traffic flow will be mixed with manual driving vehicles and ICVs. A mixed traffic flow is a traffic flow state lying between autonomous and manual traffic flows. In order to describe the car-following characteristics in a mixed traffic flow, the cooperative adaptive cruise control (CACC) car-following model and the intelligent driver model (IDM) were adopted. The car-following characteristics of different platoons from these two car-following models were analyzed. The CACC mixing ratio was used to describe the mixed traffic flow. The fixed states and disturbance states of the car-following platoons were simulated. The fixed states can be divided into three categories: the steady state, acceleration state, and deceleration state. The effects of different car-following cases and different mixing ratios on mixed traffic flow in different states were discussed. The results show that (1) in the steady state with a smaller mixing ratio, the operating speed and traffic volume of the mixed traffic flow were positively correlated. The overall traffic volume decreased with the increase in the mixing ratio, and the gap gradually narrowed. At a larger mixing ratio, the operating speed and traffic volume were negatively correlated. The overall traffic volume increased with the increase in the mixing ratio. (2) In the acceleration state, the maximum traffic volume in the platoon and the optimal mixing ratio were linearly related to the acceleration. (3) In the deceleration state with a fixed mixing ratio, the traffic volume decreased with the increase in the deceleration, with slight differences in the changing trend of the volume of the mixed flow. Under disturbances, the mixed traffic volume was positively correlated with the mixing ratio, i.e., at a larger mixing ratio, the anti-interference ability of the mixed traffic flow was higher.

Suggested Citation

  • Chengju Song & Hongfei Jia, 2022. "Multi-State Car-Following Behavior Simulation in a Mixed Traffic Flow for ICVs and MDVs," Sustainability, MDPI, vol. 14(20), pages 1-12, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13562-:d:948156
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13562/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13562/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cong Zhai & Weitiao Wu, 2019. "Car-following model based delay feedback control method with the gyroidal road," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(09), pages 1-14, September.
    2. An, Shuke & Xu, Liangjie & Qian, Lianghui & Chen, Guojun & Luo, Haoshun & Li, Fu, 2020. "Car-following model for autonomous vehicles and mixed traffic flow analysis based on discrete following interval," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    3. Jiang, Nan & Yu, Bin & Cao, Feng & Dang, Pengfei & Cui, Shaohua, 2021. "An extended visual angle car-following model considering the vehicle types in the adjacent lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    4. Kanagaraj, Venkatesan & Treiber, Martin, 2018. "Self-driven particle model for mixed traffic and other disordered flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1-11.
    5. Zhong, Zijia & Lee, Joyoung, 2019. "The effectiveness of managed lane strategies for the near-term deployment of cooperative adaptive cruise control," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 257-270.
    6. Zhu, Wen-Xing & Zhang, H.M., 2018. "Analysis of feedback control scheme on discrete car-following system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 322-330.
    7. Bowen Gong & Fanting Wang & Ciyun Lin & Dayong Wu, 2022. "Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    8. Yunze Wang & Ranran Xu & Ke Zhang, 2022. "A Car-Following Model for Mixed Traffic Flows in Intelligent Connected Vehicle Environment Considering Driver Response Characteristics," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    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. 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.
    2. Wang, Shutong & Zhu, Wen-Xing, 2022. "Modeling the heterogeneous traffic flow considering mean expected velocity field and effect of two-lane communication under connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. Huanping Li & Jian Wang & Guopeng Bai & Xiaowei Hu, 2021. "Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads," Energies, MDPI, vol. 14(12), pages 1-21, June.
    4. Sun, Qipeng & Cheng, Qianqian & Wang, Yongjie & Li, Tao & Ma, Fei & Yao, Zhigang, 2022. "Zip-merging behavior at Y-intersection based on intelligent travel points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    5. Cheng-Ju Song & Hong-Fei Jia, 2022. "Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control," Sustainability, MDPI, vol. 14(21), pages 1-12, October.
    6. Zhai, Cong & Wu, Weitiao, 2021. "A continuous traffic flow model considering predictive headway variation and preceding vehicle’s taillight effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    7. Hossain, Md. Anowar & Tanimoto, Jun, 2022. "A microscopic traffic flow model for sharing information from a vehicle to vehicle by considering system time delay effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    8. Nagahama, Akihito & Wada, Takahiro & Yanagisawa, Daichi & Nishinari, Katsuhiro, 2021. "Detection of leader–follower combinations frequently observed in mixed traffic with weak lane-discipline," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    9. Wang, Jufeng & Sun, Fengxin & Ge, Hongxia, 2018. "Effect of the driver’s desire for smooth driving on the car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 96-108.
    10. Meng, Jingwei & Jin, Yanfei & Xu, Meng, 2023. "Stochastic dynamics of a discrete-time car-following model and its time-delayed feedback control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    11. Li Zhang & Dayi Qu & Xiaojing Zhang & Shouchen Dai & Qikun Wang, 2024. "Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios," Sustainability, MDPI, vol. 16(5), pages 1-18, February.
    12. Jafaripournimchahi, Ammar & Cai, Yingfeng & Wang, Hai & Sun, Lu & Yang, Biao, 2022. "Stability analysis of delayed-feedback control effect in the continuum traffic flow of autonomous vehicles without V2I communication," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    13. Toan, Trinh Dinh & Lam, Soi Hoi & Wong, Yiik Diew & Meng, Meng, 2022. "Development and validation of a driving simulator for traffic control using field data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    14. Yu, Bin & Zhou, Huixin & Wang, Lin & Wang, Zirui & Cui, Shaohua, 2021. "An extended two-lane car-following model considering the influence of heterogeneous speed information on drivers with different characteristics under honk environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    15. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2019. "A car-following model considering the effect of electronic throttle opening angle over the curved road," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    16. Huang, Wei & Hu, Yang, 2022. "A modified cell transmission model considering queuing characteristics for channelized zone at signalized intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    17. Huimin Liu & Rongjun Cheng & Tingliu Xu, 2021. "Analysis of a Novel Two-Dimensional Lattice Hydrodynamic Model Considering Predictive Effect," Mathematics, MDPI, vol. 9(19), pages 1-13, October.
    18. Yang, Qiaoli & Shi, Zhongke & Tang, Min-an & Gao, Fengyang & Yu, Shaowei, 2019. "Modeling the permissive-only left-turn queue at signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 315-325.
    19. Zhang, Peng & Zhu, Huibing & Zhou, Yijiang, 2022. "Modeling cooperative driving strategies of automated vehicles considering trucks’ behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    20. Zhang, Qianran & Ma, Shoufeng & Tian, Junfang & Rose, John M. & Jia, Ning, 2022. "Mode choice between autonomous vehicles and manually-driven vehicles: An experimental study of information and reward," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 24-39.

    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:gam:jsusta:v:14:y:2022:i:20:p:13562-:d:948156. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.