Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2025

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/

Keywords

Trajectory Recovery, Spatio-Temporal Data Mining, Deep Learning

Discipline

Artificial Intelligence and Robotics

Areas of Excellence

Digital transformation

Publication

IEEE Transactions on Intelligent Transportation Systems

Volume

26

Issue

10

First Page

16584

Last Page

16601

ISSN

1524-9050

Identifier

10.1109/TITS.2025.3574100

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TITS.2025.3574100

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