IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v622y2023ics0378437123003825.html
   My bibliography  Save this article

Car-following modeling based on Morse model with consideration of road slope in connected vehicles environment

Author

Listed:
  • Yin, Jiacheng
  • Li, Zongping
  • Cao, Peng
  • Li, Linheng
  • Ju, Yanni

Abstract

To develop connected and automated transportation system, it is essential to model the car-following behavior of connected vehicles (CVs). In recent years, car-following models based on potential fields have attracted increasing attention owing to their objectivity, universality, variability, and measurability. However, existing potential-field-based car-following models do not consider unified gravity and repulsion between vehicles, and they lack scalability to other driving behaviors, including lane-changing and overtaking. In this study, we proposed a multiple risk potential-field-based car-following model (MRPFM) for traffic flow in CVs environment, which integrated multiple risk potential fields of traffic subjects, including road markings, road longitudinal slopes, and vehicle interactions. This model revealed the relationships between the potential field, interaction force, and driving risk. In particular, the Morse model was applied to formulate the risk potential field of vehicle interaction, and the risk potential field of the road longitudinal slope was derived using force analysis. The experiments were conducted based on the Zen Traffic Data dataset, which contained precise and complete trajectories of all vehicles along a 2 km freeway segment of longitudinal slope. We conducted a comparative analysis between the MRPFM and four prevailing car-following models—that is, the optimal velocity model, the full velocity difference model, the intelligent driver model, and the driving risk potential-field model. The results showed that the MRPFM achieved the best performance in terms of accuracy and stability.

Suggested Citation

  • Yin, Jiacheng & Li, Zongping & Cao, Peng & Li, Linheng & Ju, Yanni, 2023. "Car-following modeling based on Morse model with consideration of road slope in connected vehicles environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    • Handle: RePEc:eee:phsmap:v:622:y:2023:i:c:s0378437123003825
    DOI: 10.1016/j.physa.2023.128827
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123003825
    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.2023.128827?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.

    References listed on IDEAS

    as
    1. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish, 2019. "Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 49-75.
    2. Komada, Kazuhito & Masukura, Shuichi & Nagatani, Takashi, 2009. "Effect of gravitational force upon traffic flow with gradients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2880-2894.
    3. Yulei Jiao & Rongjun Cheng & Hongxia Ge, 2020. "A New Continuum Model considering Driving Behaviors and Electronic Throttle Effect on a Gradient Highway," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-22, May.
    4. Zhu, Wen-Xing & Yu, Rui-Ling, 2012. "Nonlinear analysis of traffic flow on a gradient highway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 954-965.
    5. Zhu, Wen-Xing & Yu, Rui-Ling, 2014. "A new car-following model considering the related factors of a gyroidal road," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 101-111.
    6. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish & Haque, Md. Mazharul, 2019. "Modelling car-following behaviour of connected vehicles with a focus on driver compliance," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 256-279.
    7. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 588-597.
    8. Jia, Yanfeng & Qu, Dayi & Song, Hui & Wang, Tao & Zhao, Zixu, 2022. "Car-following characteristics and model of connected autonomous vehicles based on safe potential field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    9. Nishi, Ryosuke & Watanabe, Takashi, 2022. "System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    10. Li, Linheng & Gan, Jing & Zhou, Kun & Qu, Xu & Ran, Bin, 2020. "A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Zhai, Cong & Li, Kening & Zhang, Ronghui & Peng, Tao & Zong, Changfu, 2024. "Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).

    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. Zhang, Futao & Qian, Yongsheng & Zeng, Junwei & Xu, Dejie & Li, Haijun, 2023. "Stability and safety analysis of mixed traffic flow considering network function degradation and platoon driving on the road with a slope," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    3. Li, Yongfu & Zhao, Hang & Zhang, Li & Zhang, Chao, 2018. "An extended car-following model incorporating the effects of lateral gap and gradient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 177-189.
    4. Jinhua Tan & Li Gong & Xuqian Qin, 2019. "Effect of Imitation Phenomenon on Two-Lane Traffic Safety in Fog Weather," IJERPH, MDPI, vol. 16(19), pages 1-15, October.
    5. Zhao, Jing & Li, Peng, 2016. "An extended car-following model with consideration of speed guidance at intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 1-8.
    6. Kaur, Ramanpreet & Sharma, Sapna, 2018. "Modeling and simulation of driver’s anticipation effect in a two lane system on curved road with slope," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 110-120.
    7. Li, Chuan-Yao & Huang, Hai-Jun & Tang, Tie-Qiao, 2017. "Analysis of user equilibrium for staggered shifts in a single-entry traffic corridor with no late arrivals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 8-18.
    8. Fu, Chuanyun & Lu, Zhaoyou & Ding, Naikan & Bai, Wei, 2024. "Distance headway-based safety evaluation of emerging mixed traffic flow under snowy weather," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    9. 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.
    10. Zhou, Wenhan & Weng, Jiancheng & Li, Tongfei & Fan, Bo & Bian, Yang, 2024. "Modeling the road network capacity in a mixed HV and CAV environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    11. Shuang Jin & Jianxi Yang & Zhongcheng Liu, 2022. "Modeling and Analysis of Car-Following for Intelligent Connected Vehicles Considering Expected Speed in Helical Ramps," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    12. Zhang, Fang & Lu, Jian & Hu, Xiaojian & Meng, Qiang, 2023. "Integrated deployment of dedicated lane and roadside unit considering uncertain road capacity under the mixed-autonomy traffic environment," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    13. Zhaobin Mo & Xuan Di & Rongye Shi, 2023. "Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection," Games, MDPI, vol. 14(1), pages 1-13, January.
    14. Zhai, Cong & Li, Kening & Zhang, Ronghui & Peng, Tao & Zong, Changfu, 2024. "Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    15. Zong, Fang & Wang, Meng & Tang, Jinjun & Zeng, Meng, 2022. "Modeling AVs & RVs’ car-following behavior by considering impacts of multiple surrounding vehicles and driving characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    16. Calvert, Simeon C. & Schakel, Wouter J. & van Lint, J.W.C., 2020. "A generic multi-scale framework for microscopic traffic simulation part II – Anticipation Reliance as compensation mechanism for potential task overload," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 42-63.
    17. Nagatani, Takashi, 2020. "Traffic flow on percolation-backbone fractal," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    18. Hao Ma & Wenhui Pei & Qi Zhang, 2022. "Research on Path Planning Algorithm for Driverless Vehicles," Mathematics, MDPI, vol. 10(15), pages 1-14, July.
    19. Yao, Zhihong & Li, Le & Liao, Wenbin & Wang, Yi & Wu, Yunxia, 2024. "Optimal lane management policy for connected automated vehicles in mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    20. Xin Chang & Xingjian Zhang & Haichao Li & Chang Wang & Zhe Liu, 2022. "A Survey on Mixed Traffic Flow Characteristics in Connected Vehicle Environments," Sustainability, MDPI, vol. 14(13), pages 1-22, June.

    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:622:y:2023:i:c:s0378437123003825. 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: 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.