Exploring Cooperative Lane Change Decisions in Vehicle-to-Infrastructure – A Potential Conflict Analysis Approach

FANG, Xushan; HUANG, Wei; ZHONG, Linkai; LIAN, Peikun; CHEN, Ning · 2026 · DOAJ

DOI: 10.7307/ptt.v38i2.967

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Summary

This paper addresses the limitations of existing lane-changing models in vehicle-to-infrastructure (V2I) environments, which often neglect the randomness of vehicle arrivals and potential conflicts in high-density traffic. Traditional models, such as Gipps or MITSIM, rely on basic relative position and speed data, failing to account for real-time road condition information provided by V2I technology. The authors propose a cooperative lane-changing decision model based on potential conflict analysis, specifically designed for mandatory lane-changing requirements at signalized intersection entrances. The goal is to mitigate conflicts, dynamically adjust lane-changing sequences, and improve both safety and traffic efficiency. The proposed model comprises three sub-models: lane-changing decision triggering, influence range calculation, and lane-changing priority determination. The study assumes all vehicles are intelligent connected vehicles with smooth information interaction. The influence range is calculated using the Intelligent Driver Model (IDM) to describe car-following behavior, defining a rectangular interaction area based on driving speed, vehicle spacing, and safety distances. To resolve conflicts, the model identifies intersecting trajectories and calculates a lane-changing priority ($P_r$) based on the number of lanes to be crossed ($P_{ln}$) and traffic density/distance to the stop line ($P_{kl}$). The weights for these factors are determined using maximum likelihood estimation, incorporating Greenberg’s logarithmic model for speed-density relationships. This allows the system to prioritize vehicles that minimize overall traffic disturbance, such as those crossing multiple lanes or those closer to the stop line in high-density conditions. Simulation experiments were conducted to verify the model’s effectiveness against real-world traffic scenarios. The results demonstrate significant improvements in traffic performance. Compared to standard lane-changing patterns, the proposed model reduced travel time by 23.30%, delays by 21.95%, and the number of stops by 23.84%. These findings indicate that the model successfully mitigates lane-changing conflicts and stabilizes traffic flow by coordinating vehicle movements through V2I data. The significance of this work lies in its provision of a novel approach for lane-changing decision-making and control in V2I environments. By integrating real-time spatiotemporal characteristics and potential conflict analysis, the model moves beyond fixed, idealized scenarios to address the dynamic nature of real-world traffic. The introduction of specific concepts like "lane-changing influence range" and "priority" offers a quantitative method for managing multi-vehicle interactions. This contributes to the field of intelligent transportation systems by offering a scalable solution for enhancing urban road network efficiency and safety under high-density conditions.

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

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• lane changing