大规模GPS轨迹数据为人类移动性提供了丰富的观测信息，但因缺乏个体层面的真实标签、GPS噪声导致的空间不确定性以及兴趣点（POI）覆盖不全，且不同出行目的在行为模式上存在根本差异，故对检测出的停留点分配出行目的仍具挑战性。本文提出一种弱监督框架，该框架整合了邻域级POI语义区域与距离加权空间似然，并针对必要性活动与非必要性活动采用差异化推断策略，同时引入多阶段帕累托优化，在无需标注标签的前提下，联合最小化推断结果与家庭出行调查统计数据之间的分布差异，并最大化推断可靠性。在洛杉矶超过8100万个停留点（staypoint）上的评估表明，相较于可比基线方法，该框架分别将活动类型频率、起始时间及持续时间的Jensen-Shannon距离（JSD）降低了23%、48%和12%。所提方法为从原始GPS轨迹生成语义标注的移动性数据提供了一条可扩展且具备不确定性感知能力的路径，适用于交通需求建模与交通政策分析。

Large-scale GPS trajectory data offer rich observations of human mobility, yet assigning trip purposes to detected stops remains challenging due to the absence of individual-level ground truth, spatial uncertainty from GPS noise and incomplete points of interest (POIs) coverage, and fundamental behavioral differences across trip purposes. We propose a weakly supervised framework integrating neighborhood-level POI semantic zones with distance-weighted spatial likelihoods, differentiated inference strategies for mandatory and non-mandatory activities, and a multi-phase Pareto optimization that jointly minimizes distributional divergence from household travel survey statistics and maximizes inference reliability without requiring annotated labels. Evaluated on over 81 million staypoints in Los Angeles, the framework reduces activity type frequency Jensen-Shannon distance (JSD) by 23%, start time JSD by 48%, and duration JSD by 12% respectively relative to a comparable baseline. The proposed approach provides a scalable and uncertainty-aware path from raw GPS trajectories to semantically annotated mobility data for travel demand modeling and transportation policy analysis.