A novel method for determining the trajectory of vulnerable road users from video recordings and its practical implications
提出一种基于目标几何和运动动力学的轨迹估计方法，比传统边界框中心法更准确，在64小时视频数据中平均PET差异0.69秒，可重新分类3.6%的冲突事件，对交通安全分析和街道空间利用有实际意义。
Accurate trajectory reconstruction is essential for reliable surrogate safety analysis, yet most computer-vision pipelines still represent road users using the geometric centre of a bounding box-a simplification that introduces systematic spatial bias. We propose a novel trajectory-estimation method that computes a representative point derived from object geometry and motion dynamics, yielding a more realistic approximation of the true occupied space. The method was evaluated on 64 h of video data covering 3 traffic environments and 223 intersections of car-pedestrian trajectories. Using Post-Encroachment Time (PET) as the primary performance indicator, we show that the proposed method produces PET values that differ from the bounding-box-centre approach by 0.69 s on average, with discrepancies exceeding 1 s in 16.7 % of all conflict events. These deviations systematically shift the classification of conflict severity, leading to up to 3.6 % of events being re-assigned to a different PET-based risk category. The results demonstrate that conventional trajectory representations may substantially distort surrogate safety metrics. Beyond safety applications, the method enables more precise estimation of the spatial footprint of different road-user types, supporting advanced analyses of street-space allocation and utilisation.