IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i2p402-d1033707.html
   My bibliography  Save this article

A Game-Theory-Based Approach to Modeling Lane-Changing Interactions on Highway On-Ramps: Considering the Bounded Rationality of Drivers

Author

Listed:
  • Weihan Chen

    (School of Transportation, Southeast University, Jiulonghu Campus, Nanjing 211100, China
    These authors contributed equally to this work.)

  • Gang Ren

    (School of Transportation, Southeast University, Jiulonghu Campus, Nanjing 211100, China)

  • Qi Cao

    (School of Transportation, Southeast University, Jiulonghu Campus, Nanjing 211100, China
    These authors contributed equally to this work.)

  • Jianhua Song

    (School of Transportation, Southeast University, Jiulonghu Campus, Nanjing 211100, China)

  • Yikun Liu

    (Faculty of Foreign Studies, Beijing Language and Culture University, Beijing 100083, China)

  • Changyin Dong

    (School of Transportation, Southeast University, Jiulonghu Campus, Nanjing 211100, China)

Abstract

In highway on-ramp sections, the conflictual interactions between a subject vehicle (merging vehicle) in the acceleration lane and a following vehicle (lagging vehicle) in the adjacent mainline can lead to traffic congestion, go–stop oscillations, and serious safety hazards. Human drivers combine their previous lane-changing experience and their perception of surrounding traffic conditions to decide whether to merge. However, the decisions that they make are not always optimal in specific traffic scenarios due to fuzzy perception and misjudgment. That is, they make lane-changing decisions in a bounded rational way. In this paper, a game-theory-based approach is used to model the interactive behavior of mandatory lane-changing in a highway on-ramp section. The model comprehensively considers vehicle interactions and the bounded rationality of drivers by modeling lane-changing behavior on on-ramps as a two-person non-zero-sum non-cooperative game with incomplete information. In addition, the Logit QRE is used to explain the bounded rationality of drivers. In order to estimate the parameters, a bi-level programming framework is built. Vehicle trajectory data from NGSIM and an unmanned aerial vehicle survey were used for model calibration and validation. The validation results were rigorously evaluated by using various performance indicators, such as the mean absolute error, root mean square error, detection rate, and false-alarm rate. It can be seen that the proposed game theory-based model was able to effectively predict merging and yielding interactions with a high degree of accuracy.

Suggested Citation

  • Weihan Chen & Gang Ren & Qi Cao & Jianhua Song & Yikun Liu & Changyin Dong, 2023. "A Game-Theory-Based Approach to Modeling Lane-Changing Interactions on Highway On-Ramps: Considering the Bounded Rationality of Drivers," Mathematics, MDPI, vol. 11(2), pages 1-16, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:402-:d:1033707
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/2/402/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/2/402/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Jia & Chen, Di & Zhang, Michael, 2022. "Equilibrium modeling of mixed autonomy traffic flow based on game theory," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 110-127.
    2. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    3. Kita, Hideyuki, 1999. "A merging-giveway interaction model of cars in a merging section: a game theoretic analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(3-4), pages 305-312, April.
    4. 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.
    5. Montanino, Marcello & Punzo, Vincenzo, 2015. "Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 82-106.
    6. Coifman, Benjamin & Li, Lizhe, 2017. "A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 362-377.
    7. 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.
    8. Dong, Shuoxuan & Zhou, Yang & Chen, Tianyi & Li, Shen & Gao, Qiantong & Ran, Bin, 2021. "An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    9. Li, Huamin & Zhang, Shun, 2022. "Lane change behavior with uncertainty and fuzziness for human driving vehicles and its simulation in mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(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. Wang, Bingtong & Li, Zhibin & Wang, Shunchao & Li, Meng & Ji, Ang, 2022. "Modeling bounded rationality in discretionary lane change with the quantal response equilibrium of game theory," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 145-161.
    2. Li, Gen & Zhao, Le & Tang, Wenyun & Wu, Lan & Ren, Jiaolong, 2023. "Modeling and analysis of mandatory lane-changing behavior considering heterogeneity in means and variances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    3. Zhou, Zhi & Li, Linheng & Qu, Xu & Ran, Bin, 2024. "A self-adaptive IDM car-following strategy considering asymptotic stability and damping characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    4. Dong, Shuoxuan & Zhou, Yang & Chen, Tianyi & Li, Shen & Gao, Qiantong & Ran, Bin, 2021. "An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    5. 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.
    6. 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.
    7. Montanino, Marcello & Punzo, Vincenzo, 2021. "On string stability of a mixed and heterogeneous traffic flow: A unifying modelling framework," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 133-154.
    8. Zhou, Yang & Ahn, Soyoung & Wang, Meng & Hoogendoorn, Serge, 2020. "Stabilizing mixed vehicular platoons with connected automated vehicles: An H-infinity approach," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 152-170.
    9. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    10. Khelfa, Basma & Ba, Ibrahima & Tordeux, Antoine, 2023. "Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    11. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    12. Yu, Yuewen & Luo, Xia & Su, Qiming & Peng, Weikang, 2023. "A dynamic lane-changing decision and trajectory planning model of autonomous vehicles under mixed autonomous vehicle and human-driven vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    13. Sheikh, Muhammad Sameer & Wang, Ji & Regan, Amelia, 2021. "A game theory-based controller approach for identifying incidents caused by aberrant lane changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    14. Ronan Keane & H. Oliver Gao, 2021. "Fast Calibration of Car-Following Models to Trajectory Data Using the Adjoint Method," Transportation Science, INFORMS, vol. 55(3), pages 592-615, May.
    15. Dong, Jiakuan & Luo, Dongyu & Gao, Zhijun & Wang, Jiangfeng & Chen, Lei, 2023. "Benefit of connectivity on promoting stability and capacity of traffic flow in automation era: An analytical and numerical investigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    16. Ji Ang & David Levinson, 2020. "A Review of Game Theory Models of Lane Changing," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    17. Mehr, Negar & Li, Ruolin & Horowitz, Roberto, 2021. "A game theoretic macroscopic model of lane choices at traffic diverges with applications to mixed–autonomy networks," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 45-59.
    18. Ke Wang & Qingwen Xue & Yingying Xing & Chongyi Li, 2020. "Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
    19. Kou, Yukang & Ma, Changxi, 2023. "Dual-objective intelligent vehicle lane changing trajectory planning based on polynomial optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    20. Bosch-Domènech, Antoni & Vriend, Nicolaas J., 2013. "On the role of non-equilibrium focal points as coordination devices," Journal of Economic Behavior & Organization, Elsevier, vol. 94(C), pages 52-67.

    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:11:y:2023:i:2:p:402-:d:1033707. 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.