IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v527y2019ics0378437119308301.html
   My bibliography  Save this article

An extended car-following model considering driver’s desire for smooth driving on the curved road

Author

Listed:
  • Sun, Yuqing
  • Ge, Hongxia
  • Cheng, Rongjun

Abstract

In real traffic, many driver’s desire to lower the fuel consumption by driving smoothly. In this paper, an extended car-following model considering the driver’s desire for smooth driving on a curved road is proposed. The desire for smooth driving can be considered as a control signal including the velocity difference between the steady and history velocity. The stability conditions are obtained by the control theory, and the modified Korteweg–de Vries (mKdV) equation is derived via the non-linear analysis method. The numerical simulations are carried out to analyze the control signal, the friction coefficient and radius of curved road effects on traffic flow. The results show that the control signal has a positive effect on improving traffic flow stability, while the stability decreases with the increase of the two other parameters.

Suggested Citation

  • Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2019. "An extended car-following model considering driver’s desire for smooth driving on the curved road," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119308301
    DOI: 10.1016/j.physa.2019.121426
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119308301
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.121426?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kang, Yi-rong & Tian, Chuan, 2024. "A new curved road lattice model integrating the multiple prediction effect under V2X environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    2. Zhang, Xiangzhou & Shi, Zhongke & Yang, Qiaoli & An, Xiaodong, 2024. "Impacts of visuo-spatial working memory on the dynamic performance and safety of car-following behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    3. Zhang, Xiangzhou & Shi, Zhongke & Yu, Shaowei & Ma, Lijing, 2023. "A new car-following model considering driver’s desired visual angle on sharp curves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    4. Kang, Chengjun & Qian, Yongsheng & Zeng, Junwei & Wei, Xuting & Zhang, Futao, 2024. "Analysis of stability, energy consumption and CO2 emissions in novel discrete-time car-following model with time delay under V2V environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    5. Zhang, Geng & Yin, Le & Pan, Dong-Bo & Zhang, Yu & Cui, Bo-Yuan & Jiang, Shan, 2020. "Research on multiple vehicles’ continuous self-delayed velocities on traffic flow with vehicle-to-vehicle communication," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    6. 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.
    7. Cui, Bo-Yuan & Zhang, Geng & Ma, Qing-Lu, 2021. "A stable velocity control strategy for a discrete-time car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    8. Chen, Jin & Sun, Dihua & Zhao, Min & Li, Yang & Liu, Zhongcheng, 2021. "DCFS-based deep learning supervisory control for modeling lane keeping of expert drivers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(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:eee:phsmap:v:527:y:2019:i:c:s0378437119308301. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.