Learning Spatio-Temporal Dynamics for Trajectory Recovery via Time-Aware Transformer

TL;DR

This paper introduces TedTrajRec, a novel framework combining a time-aware Transformer and a graph neural network to improve GPS trajectory recovery by modeling complex spatio-temporal dynamics, especially for irregularly sampled data.

Contribution

It proposes a new method, TedTrajRec, integrating PD-GNN and TedFormer to better capture spatio-temporal dynamics for trajectory recovery, surpassing previous models.

Findings

Outperforms existing methods on three real-world datasets.

Effectively models irregularly sampled GPS data.

Demonstrates superior trajectory recovery accuracy.

Abstract

In real-world applications, GPS trajectories often suffer from low sampling rates, with large and irregular intervals between consecutive GPS points. This sparse characteristic presents challenges for their direct use in GPS-based systems. This paper addresses the task of map-constrained trajectory recovery, aiming to enhance trajectory sampling rates of GPS trajectories. Previous studies commonly adopt a sequence-to-sequence framework, where an encoder captures the trajectory patterns and a decoder reconstructs the target trajectory. Within this framework, effectively representing the road network and extracting relevant trajectory features are crucial for overall performance. Despite advancements in these models, they fail to fully leverage the complex spatio-temporal dynamics present in both the trajectory and the road network. To overcome these limitations, we categorize the spatio-temporal dynamics of trajectory data into two distinct aspects: spatial-temporal traffic dynamics and trajectory dynamics. Furthermore, We propose TedTrajRec, a novel method for trajectory recovery. To capture spatio-temporal traffic dynamics, we introduce PD-GNN, which models periodic patterns and learns topologically aware dynamics concurrently for each road segment. For spatio-temporal trajectory dynamics, we present TedFormer, a time-aware Transformer that incorporates temporal dynamics for each GPS location by integrating closed-form neural ordinary differential equations into the attention mechanism. This allows TedFormer to effectively handle irregularly sampled data. Extensive experiments on three real-world datasets demonstrate the superior performance of TedTrajRec. The code is publicly available at https://github.com/ysygMhdxw/TEDTrajRec/.