Restoring Super-High Resolution GPS Mobility Data

TL;DR

This paper introduces a transformer and GCN-based system for reconstructing high-resolution GPS trajectories from low-resolution data, enhancing accuracy while preserving user privacy.

Contribution

It presents a novel deep learning architecture combining transformers and GCNs for trajectory reconstruction, outperforming traditional methods.

Findings

Achieves an average Fréchet distance of 0.198 km on Beijing dataset.

Outperforms map-matching algorithms with 0.632 km distance.

Generalizes well to synthetic trajectory data.

Abstract

This paper presents a novel system for reconstructing high-resolution GPS trajectory data from truncated or synthetic low-resolution inputs, addressing the critical challenge of balancing data utility with privacy preservation in mobility applications. The system integrates transformer-based encoder-decoder models with graph convolutional networks (GCNs) to effectively capture both the temporal dependencies of trajectory data and the spatial relationships in road networks. By combining these techniques, the system is able to recover fine-grained trajectory details that are lost through data truncation or rounding, a common practice to protect user privacy. We evaluate the system on the Beijing trajectory dataset, demonstrating its superior performance over traditional map-matching algorithms and LSTM-based synthetic data generation methods. The proposed model achieves an average Fr\'echet distance of 0.198 km, significantly outperforming map-matching algorithms (0.632 km) and synthetic trajectory models (0.498 km). The results show that the system is not only capable of accurately reconstructing real-world trajectories but also generalizes effectively to synthetic data. These findings suggest that the system can be deployed in urban mobility applications, providing both high accuracy and robust privacy protection.