IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i6p1014-d1616735.html
   My bibliography  Save this article

Mathematical Modeling and Parameter Estimation of Lane-Changing Vehicle Behavior Decisions

Author

Listed:
  • Jianghui Wen

    (School of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China)

  • Yebei Xu

    (School of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China)

  • Min Dai

    (School of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China)

  • Nengchao Lyu

    (Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Lane changing is a crucial scenario in traffic environments, and accurately recognizing and predicting lane-changing behavior is essential for ensuring the safety of both autonomous vehicles and drivers. Through considering the multi-vehicle information interaction characteristics in lane-changing behavior for vehicles and the impact of driver experience needs on lane-changing decisions, this paper proposes a lane-changing model for vehicles to achieve safe and comfortable driving. Firstly, a lane-changing intention recognition model incorporating interaction effects was established to obtain the initial lane-changing intention probability of the vehicles. Secondly, by accounting for individual driving styles, a lane-changing behavior decision model was constructed based on a Gaussian mixture hidden Markov model (GMM-HMM) along with a parameter estimation method. The initial lane-changing intention probability serves as the input for the decision model, and the final lane-changing decision is made by comparing the probabilities of lane-changing and non-lane-changing scenarios. Finally, the model was validated using real-world data from the Next Generation Simulation (NGSIM) dataset, with empirical results demonstrating its high accuracy in recognizing and predicting lane-changing behavior. This study provides a robust framework for enhancing lane-changing decision making in complex traffic environments.

Suggested Citation

  • Jianghui Wen & Yebei Xu & Min Dai & Nengchao Lyu, 2025. "Mathematical Modeling and Parameter Estimation of Lane-Changing Vehicle Behavior Decisions," Mathematics, MDPI, vol. 13(6), pages 1-20, March.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:1014-:d:1616735
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/6/1014/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/6/1014/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deng, Jian-Hua & Feng, Huan-Huan, 2019. "A multilane cellular automaton multi-attribute lane-changing decision model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 529(C).
    2. Gipps, P. G., 1986. "A model for the structure of lane-changing decisions," Transportation Research Part B: Methodological, Elsevier, vol. 20(5), pages 403-414, October.
    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. Bowen Gong & Zhipeng Xu & Ruixin Wei & Tao Wang & Ciyun Lin & Peng Gao, 2023. "Reinforcement Learning-Based Lane Change Decision for CAVs in Mixed Traffic Flow under Low Visibility Conditions," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    2. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Wang, Jinghui & Lv, Wei & Jiang, Yajuan & Qin, Shuangshuang & Li, Jiawei, 2021. "A multi-agent based cellular automata model for intersection traffic control simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    4. Minh Sang Pham Do & Ketoma Vix Kemanji & Man Dinh Vinh Nguyen & Tuan Anh Vu & Gerrit Meixner, 2023. "The Action Point Angle of Sight: A Traffic Generation Method for Driving Simulation, as a Small Step to Safe, Sustainable and Smart Cities," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    5. Bonsall, Peter & Liu, Ronghui & Young, William, 2005. "Modelling safety-related driving behaviour--impact of parameter values," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(5), pages 425-444, June.
    6. Hiroshi Tatsumi & Masaya Kawano & Tetsunobu Yoshitake & Satoshi Toi & Yoshitaka Kajita, 2004. "Evaluation of City Planning Road Development Measures by Microscopic Traffic Simulation," ERSA conference papers ersa04p221, European Regional Science Association.
    7. Mingmin Guo & Zheng Wu & Huibing Zhu, 2018. "Empirical study of lane-changing behavior on three Chinese freeways," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
    8. 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).
    9. Ziakopoulos, Apostolos & Oikonomou, Maria G. & Vlahogianni, Eleni I. & Yannis, George, 2021. "Quantifying the implementation impacts of a point to point automated urban shuttle service in a large-scale network," Transport Policy, Elsevier, vol. 114(C), pages 233-244.
    10. Wang, Zhaohan & Ramezani, Mohsen & Levinson, David, 2024. "How mandatory are ‘Mandatory’ lane changes? An analytical and experimental study on the costs of missing freeway exits," Transportation Research Part B: Methodological, Elsevier, vol. 186(C).
    11. Kai Nagel & Peter Wagner & Richard Woesler, 2003. "Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling," Operations Research, INFORMS, vol. 51(5), pages 681-710, October.
    12. Espadaler-Clapés, Jasso & Barmpounakis, Emmanouil & Geroliminis, Nikolas, 2023. "Empirical investigation of lane usage, lane changing and lane choice phenomena in a multimodal urban arterial," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    13. Sun, Baofeng & Ma, Guodong & Song, Jia & Cheng, Zeyang & Wang, Wei, 2023. "Driving safety field modeling focused on heterogeneous traffic flows and cooperative control strategy in highway merging zone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    14. Liu, Hongjie & Yuan, Tengfei & Zeng, Xiaoqing & Guo, KaiYi & Wang, Yizeng & Mo, Yanghui & Xu, Hongzhe, 2024. "Eco-driving strategy for connected automated vehicles in mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    15. Coifman, Benjamin, 2006. "Extracting More Information from the Existing Freeway Traffic Monitoring Infrastructure," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt34n479gz, Institute of Transportation Studies, UC Berkeley.
    16. 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.
    17. Jin, Wen-Long, 2012. "A kinematic wave theory of multi-commodity network traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 1000-1022.
    18. Ang Ji & David Levinson, 2021. "Estimating the Social Gap with a Game Theory Model of Lane Changing," Working Papers 2021-02, University of Minnesota: Nexus Research Group.
    19. Taghreed Alghamdi & Sifatul Mostafi & Ghadeer Abdelkader & Khalid Elgazzar, 2022. "A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior," Future Internet, MDPI, vol. 14(10), pages 1-21, October.
    20. Jin, Wen-Long, 2010. "A kinematic wave theory of lane-changing traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 1001-1021, September.

    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:jmathe:v:13:y:2025:i:6:p:1014-:d:1616735. 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.