IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14067-d956402.html
   My bibliography  Save this article

Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control

Author

Listed:
  • Cheng-Ju Song

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

  • Hong-Fei Jia

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

Abstract

This study aims to improve the desired distance adaptability of the cooperative adaptive cruise control (CACC) during car-following. In this study, the characteristics of the desired distance in different traffic flow states were analyzed. The general functional form of the desired distance in the car-following process was formulated. Then, a car-following platoon was constructed to compare the car-following effect of the platoon under different conditions, using the following speed and the lead vehicle disturbance, as the observed variable and the simulation condition, respectively. The car-following effect of the platoon under different parameters was also compared, based on the improved CACC model. The results show that the improved CACC model exhibited more advantages in car-following efficiency, it can better describe the state of the car-following queue under different traffic flow parameters and car-following behavior conditions, it has a strong anti-interference ability for the fluctuation of the car-following queue and is conducive to further improving the intelligent operation of car-following queue.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14067-:d:956402
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14067/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14067/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. He, Zhengbing & Zheng, Liang & Guan, Wei, 2015. "A simple nonparametric car-following model driven by field data," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 185-201.
    2. Wu, Lina & Ci, Yusheng & Wang, Yunpeng & Chen, Peng, 2020. "Fuel consumption at the oversaturated signalized intersection considering queue effects: A case study in Harbin, China," Energy, Elsevier, vol. 192(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. Wei, Dali & Liu, Hongchao, 2013. "Analysis of asymmetric driving behavior using a self-learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 1-14.
    5. Ci, Yusheng & Wu, Lina & Zhao, Jiafa & Sun, Yichen & Zhang, Guohui, 2019. "V2I-based car-following modeling and simulation of signalized intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 672-679.
    6. Sun, Jie & Zheng, Zuduo & Sun, Jian, 2018. "Stability analysis methods and their applicability to car-following models in conventional and connected environments," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 212-237.
    7. Wang, Xiaoning & Liu, Minzhuang & Ci, Yusheng & Wu, Lina, 2022. "Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(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. Shuaiyang Jiao & Shengrui Zhang & Bei Zhou & Zixuan Zhang & Liyuan Xue, 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    2. Wang, Xiaoning & Liu, Minzhuang & Ci, Yusheng & Wu, Lina, 2022. "Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. He, Jia & He, Zhengbing & Fan, Bo & Chen, Yanyan, 2020. "Optimal location of lane-changing warning point in a two-lane road considering different traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    4. Yongyi Li & Wei Yang & Xiaorui Zhang & Xi Kang & Mengfei Li, 2022. "Research on Automatic Driving Trajectory Planning and Tracking Control Based on Improvement of the Artificial Potential Field Method," Sustainability, MDPI, vol. 14(19), pages 1-28, September.
    5. Cui, Ziyu & Wang, Xiaoning & Ci, Yusheng & Yang, Changyun & Yao, Jia, 2023. "Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    6. 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).
    7. Zhao, Fangxia & Shang, HuaYan & Cui, JiHui, 2023. "Role of electric vehicle driving behavior on optimal setting of wireless charging lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    8. Yao, Handong & Li, Qianwen & Li, Xiaopeng, 2020. "A study of relationships in traffic oscillation features based on field experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 339-355.
    9. He, Zhengbing & Zheng, Liang & Guan, Wei, 2015. "A simple nonparametric car-following model driven by field data," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 185-201.
    10. Yao, Zhihong & Xu, Taorang & Jiang, Yangsheng & Hu, Rong, 2021. "Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    11. Hamedi, Hamidreza & Shad, Rouzbeh & Ziaee, Seyed Ali, 2022. "A comparative study on measurement of lane-changing trajectory similarities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    12. Manivasakan, Hesavar & Kalra, Riddhi & O'Hern, Steve & Fang, Yihai & Xi, Yinfei & Zheng, Nan, 2021. "Infrastructure requirement for autonomous vehicle integration for future urban and suburban roads – Current practice and a case study of Melbourne, Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 36-53.
    13. Yanmin Qi & Zuduo Zheng & Dongyao Jia, 2020. "Exploring the Spatial-Temporal Relationship between Rainfall and Traffic Flow: A Case Study of Brisbane, Australia," Sustainability, MDPI, vol. 12(14), pages 1-24, July.
    14. Tian, Junfang & Li, Guangyu & Treiber, Martin & Jiang, Rui & Jia, Ning & Ma, Shoufeng, 2016. "Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 560-575.
    15. Ding, Heng & Pan, Hao & Bai, Haijian & Zheng, Xiaoyan & Chen, Jin & Zhang, Weihua, 2022. "Driving strategy of connected and autonomous vehicles based on multiple preceding vehicles state estimation in mixed vehicular traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    16. Sun, Jie & Zheng, Zuduo & Sun, Jian, 2020. "The relationship between car following string instability and traffic oscillations in finite-sized platoons and its use in easing congestion via connected and automated vehicles with IDM based control," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 58-83.
    17. Saifuzzaman, Mohammad & Zheng, Zuduo & Haque, Md. Mazharul & Washington, Simon, 2017. "Understanding the mechanism of traffic hysteresis and traffic oscillations through the change in task difficulty level," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 523-538.
    18. Dayi Qu & Shaojie Wang & Haomin Liu & Yiming Meng, 2022. "A Car-Following Model Based on Trajectory Data for Connected and Automated Vehicles to Predict Trajectory of Human-Driven Vehicles," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    19. 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).
    20. Han, Yu & Zhang, Mingyu & Guo, Yanyong & Zhang, Le, 2022. "A streaming-data-driven method for freeway traffic state estimation using probe vehicle trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).

    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:21:p:14067-:d:956402. 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.