Weaving segments, integral to expressway systems, face safety challenges due to frequent lane changes. While previous research has leveraged pre– and post–lane-changing (LC) data for risk estimation, few efforts have assessed time-series characteristics of LC conflict risks. This study proposes an approach to evaluate safety performance of weaving segments by investigating conflict risk patterns during the LC process using vehicle trajectory data. First, risk profiles are generated based on driving safety field theory for detected LC conflicts. These profiles are then categorized into distinct time-series patterns using k-shape clustering. Finally, determinants of these patterns are identified by enhanced random forest, which provides valuable insights into their relationship with risk severity. Analysis of trajectory data from 12 weaving segments in China reveals three main risk patterns (ascent, descent, and stabilization with a peak) for both rear-end and sideswipe conflicts. Ascending risk patterns signify more severe conflicts compared to descending ones. Increasing auxiliary lanes at weaving segments results in a more evenly distributed acceleration, potentially leading to a more severe LC conflict. The lowest risk is observed at the weaving segments with two auxiliary lanes. This study offers a novel perspective for mitigating LC crashes and enhancing traffic safety in weaving segments.
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