用新模型从稀疏定位数据中还原个人活动序列
Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
- 融合插入变换器与变量选择网络，动态补全缺失活动段
- 补全的活动模式更多样真实，过渡更贴近实际
- 适合做人流分析、城市规划等需精准轨迹研究的场景
基于位置的服务（LBS）数据为人类移动行为研究提供关键信息，但其稀疏性常导致行程与活动序列不完整，难以准确推断出行程与活动。本文提出新问题：能否利用高质量LBS数据中的活动序列，恢复个体层面的不完整活动序列？为此，提出一种新型模型VSNIT（Variable Selection Network-fused Insertion Transformer），将插入变换器的灵活序列构建能力与变量选择网络的动态协变量处理能力相结合，以补全不完整活动序列，同时保留已有数据。实验表明，VSNIT能插入更多样化、更真实的活动模式，更贴近真实世界变化；在打断的活动转换恢复上表现更优，与目标序列对齐度更高。在所有指标上均显著优于基线模型。结果表明，VSNIT在活动序列恢复任务中兼具高精度与多样性，展现了提升LBS数据在移动性分析中应用潜力的前景。
原文摘要 · Abstract (English)
Location-Based Service (LBS) data provides critical insights into human mobility, yet its sparsity often yields incomplete trip and activity sequences, making accurate inferences about trips and activities difficult. We raise a research problem: Can we use activity sequences derived from high-quality LBS data to recover incomplete activity sequences at the individual level? This study proposes a new solution, the Variable Selection Network-fused Insertion Transformer (VSNIT), integrating the Insertion Transformer's flexible sequence construction with the Variable Selection Network's dynamic covariate handling capability, to recover missing segments in incomplete activity sequences while preserving existing data. The findings show that VSNIT inserts more diverse, realistic activity patterns, more closely matching real-world variability, and restores disrupted activity transitions more effectively aligning with the target. It also performs significantly better than the baseline model across all metrics. These results highlight VSNIT's superior accuracy and diversity in activity sequence recovery tasks, demonstrating its potential to enhance LBS data utility for mobility analysis. This approach offers a promising framework for future location-based research and applications.
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