用地理语义区和不确定性校准，从GPS轨迹自动推断出行目的
Uncertainty-Aware Trip Purpose Inference from GPS Trajectories via POI Semantic Zones and Pareto Calibration
- 基于POI语义区与距离加权，构建弱监督推断框架
- 在洛杉矶数据上使活动类型、出发时间等分布误差降低12%-48%
- 适合交通需求建模与政策分析，可处理原始轨迹的噪声和缺失
大规模GPS轨迹数据提供了人类移动行为的丰富观测，但为检测到的停留点分配出行目的仍具挑战，原因包括缺乏个体层面的真实标签、GPS噪声带来的空间不确定性、兴趣点（POIs）覆盖不全，以及不同出行目的间的行为本质差异。本文提出一种弱监督框架，融合邻域级POI语义区与距离加权空间似然，对强制性与非强制性活动采用差异化推断策略，并通过多阶段帕累托优化，联合最小化与家庭出行调查统计分布的差异，同时最大化推断可靠性，无需标注标签。在洛杉矶超过8100万次停留点上评估，相较于可比基线，活动类型频率的詹森-香农散度（JSD）降低23%，出发时间JSD降低48%，持续时间JSD降低12%。该方法为从原始GPS轨迹生成语义标注的移动数据提供了一条可扩展且考虑不确定性的路径，适用于出行需求建模与交通政策分析。
原文摘要 · Abstract (English)
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.
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