Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model

TL;DR

This paper introduces VSNIT, a novel transformer-based model that effectively recovers missing segments in individual activity sequences from sparse LBS data, improving accuracy and diversity of mobility insights.

Contribution

The study presents VSNIT, combining the Insertion Transformer with a Variable Selection Network, to enhance activity sequence recovery from incomplete LBS data.

Findings

VSNIT inserts more diverse, realistic activity patterns.

VSNIT more accurately restores activity transitions.

Outperforms baseline models across all metrics.

Abstract

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.