Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
This work addresses a domain-specific issue for mobility analysis by improving LBS data utility, though it appears incremental as it builds on existing transformer and variable selection methods.
The study tackled the problem of incomplete activity sequences in Location-Based Service (LBS) data by proposing the VSNIT model, which recovered missing segments with more diverse and realistic patterns, performing significantly better than baselines across all metrics.
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.