Identification and Traffic Impact Analysis of Merging Behavior in Expressway Weaving Areas

The complex traffic behaviors in urban expressway weaving areas significantly impact traffic efficiency and safety. To deeply investigate the interaction characteristics between vehicles merging from mainlines and auxiliary roads, this study, based on large-scale vehicle trajectory data from the Luoshi Road Elevated Expressway in Wuhan, systematically proposes and defines four interactive lane-changing patterns: competitive, cooperative-competitive, cooperative, and competitive-cooperative. This classification framework, by innovatively introducing surrogate safety measures (SSMs) for quantitative constraints, comprehensively covering key parameters such as time-to-collision (TTC), following distance (GAP), vehicle speed differential, and lateral offset of vehicles. To achieve accurate and automated identification of these lane-changing patterns, the study constructs and optimizes a behavior identification model based on the eXtreme Gradient Boosting (XGBoost) algorithm. During model construction, rigorous data filtering addressed the ambiguity of data constraints found in traditional research by removing confounding data such as intersecting merges, ultimately resulting in 1 049 typical, stable merging cases for modeling. The experimental results show that the model achieves an accuracy of 91.83%. Further analysis of traffic flow impacts reveals that the competitive-cooperative lane-changing pattern demonstrates the best overall benefits in enhancing lane-changing efficiency and ensuring driving safety. It achieves this by optimizing the cooperative and competitive relationships between vehicles, which effectively reduces the risk of traffic conflicts.