Research on conflict analysis and prediction of unsignalized intersections based on multiple alternative safety measures

作者：Jiafu Yang, Ting Wang, Rongjun Cheng · 发表于：Chaos Solitons & Fractals · 年份：2026 · DOI：10.1016/j.chaos.2026.118006 · 研究领域：Traffic and Road Safety、Evaluation and Optimization Models、Research studies in Vietnam

Unsignalized intersections, due to the frequent interactions between traffic participants, have become high-risk areas for road safety management. In recent years, the continued growth in the number of non-motorized vehicles (NMV), particularly those on electric vehicles, has exacerbated safety issues on these roads. This study, focusing on a typical unsignalized intersection, used drone aerial photography to capture high-angle video data. Combined with computer vision-based trajectory extraction techniques, the study employed multiple alternative safety metrics to quantitatively assess traffic conflicts at the intersection. Subsequently, the machine learning-based traffic conflict prediction model was constructed using the identified conflict events as a sample. The results showed that potential conflicts were primarily concentrated at intersection entrances and exits, with right-turn exits being the most frequent. The conflict risk and severity of non-motor vehicles interacting with motor vehicles were significantly higher than those of other traffic participants, and the rate of both parties yielding in high-risk situations was lower. Among the five prediction models, XGBoost performed best in terms of accuracy (83.6%), precision, and generalization performance. These results demonstrate that the conflict identification framework based on drone video and machine learning can effectively support proactive safety assessments at unsignalized intersections, providing data supp...