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Satisfaction-Based Optimal Lane Change Modelling of Mixed Traffic Flow and Intersection Vehicle Guidance Control Method in an Intelligent and Connected Environment

Author

Listed:
  • Luxi Dong

    (College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China
    Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin 541004, China
    College of Earth Sciences, Guilin University of Technology, Guilin 541004, China)

  • Xiaolan Xie

    (College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China
    Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin 541004, China)

  • Lieping Zhang

    (School of Mechanical Engineering, Guilin University of Aerospace Technology, Guilin 541004, China)

  • Xiaohui Cheng

    (College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China
    Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin 541004, China)

  • Bin Qiu

    (College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China
    Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin 541004, China)

Abstract

The information interaction characteristics of connected vehicles are distinct from those of non-connected vehicles, thereby exerting an influence on the conventional traffic flow model. The original lane-changing model for non-connected vehicles is no longer applicable in the context of the new traffic flow environment. The modelling of the new hybrid traffic flow, comprising both connected and ordinary vehicles, is set to be a pivotal research topic in the coming years. The objective of this paper is to present a methodology for optimal mixed traffic flow dynamic modelling and cooperative control in intelligent and connected environments (ICE). The study utilizes the real-time perception and information interaction of connected vehicles for traffic information, taking into account the access characteristics of both connected and non-connected vehicles. The satisfaction-based free lane-changing and mandatory lane-changing models of connected vehicles are designed. Secondly, a mixed traffic flow lane-changing model based on influence characteristics is constructed for the influence area of connected vehicles. This model takes into account the degree of influence that connected vehicles have on non-connected vehicles, with different distances being considered respectively. Subsequently, a vehicle guidance strategy for mixed traffic flows comprising grid-connected and conventional vehicles is proposed. A variety of speed guidance scenarios are considered, with an in-depth analysis of the speed optimization of connected vehicles and the movement law of non-connected vehicles. This comprehensive analysis forms the foundation for the development of a vehicle guidance strategy for mixed traffic flows, with the overarching objective being to minimize the average delay of vehicles. In order to evaluate the effectiveness of the proposed method, the intersection of Gaota Road and Fangshui North Street in Yanqing District, Beijing, has been selected for analysis. The results of the study demonstrate that by modifying the density of the mixed traffic flow, the overall average speed of the mixed traffic flow declines as the density of vehicles increases. The findings reported in this study reflect the role of connected vehicles in enhancing road capacity, maximizing intersection capacity and mitigating the occurrence of queuing phenomena, and improving travel speed through the mixed traffic flow lane-changing model based on impact characteristics. This study also provides some guidance for future control of the mixed traffic flow formed by emergency vehicles and social vehicles and for realizing a smart city.

Suggested Citation

  • Luxi Dong & Xiaolan Xie & Lieping Zhang & Xiaohui Cheng & Bin Qiu, 2025. "Satisfaction-Based Optimal Lane Change Modelling of Mixed Traffic Flow and Intersection Vehicle Guidance Control Method in an Intelligent and Connected Environment," Sustainability, MDPI, vol. 17(3), pages 1-42, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1077-:d:1579187
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    References listed on IDEAS

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