Blurred Encoding for Trajectory Representation Learning

2025-11-11

AI Summary

Existing trajectory representation learning methods coarsely map GPS trajectories onto grids or road segments, resulting in significant loss of fine-grained spatiotemporal information. To address this, we propose BLUE—a multi-scale trajectory block construction method based on fuzzy encoding—that hierarchically represents trajectories via progressive coordinate precision decay. BLUE employs an encoder-decoder pyramid architecture integrating Transformers with upsampling and downsampling modules, jointly optimizing local geometric details and global mobility patterns under an unsupervised trajectory reconstruction objective. The model is trained end-to-end using mean squared error as the loss function. Evaluated on three downstream tasks—including trajectory classification, clustering, and next-location prediction—BLUE outperforms eight state-of-the-art methods, achieving an average accuracy improvement of 30.90%. It effectively alleviates the representational imbalance between fine-grained spatial details and high-level semantic patterns in trajectory modeling.

Technology Category

• Machine Learning: Representation Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Trajectory representation learning (TRL) maps trajectories to vector embeddings and facilitates tasks such as trajectory classification and similarity search. State-of-the-art (SOTA) TRL methods transform raw GPS trajectories to grid or road trajectories to capture high-level travel semantics, i.e., regions and roads. However, they lose fine-grained spatial-temporal details as multiple GPS points are grouped into a single grid cell or road segment. To tackle this problem, we propose the BLUrred Encoding method, dubbed BLUE, which gradually reduces the precision of GPS coordinates to create hierarchical patches with multiple levels. The low-level patches are small and preserve fine-grained spatial-temporal details, while the high-level patches are large and capture overall travel patterns. To complement different patch levels with each other, our BLUE is an encoder-decoder model with a pyramid structure. At each patch level, a Transformer is used to learn the trajectory embedding at the current level, while pooling prepares inputs for the higher level in the encoder, and up-resolution provides guidance for the lower level in the decoder. BLUE is trained using the trajectory reconstruction task with the MSE loss. We compare BLUE with 8 SOTA TRL methods for 3 downstream tasks, the results show that BLUE consistently achieves higher accuracy than all baselines, outperforming the best-performing baselines by an average of 30.90%. Our code is available at https://github.com/slzhou-xy/BLUE.

Problem

• Loss of fine-grained spatial-temporal details in trajectory representation learning
• Grouping multiple GPS points into single grid cells loses precision
• Need to preserve both detailed patterns and overall travel semantics

Innovation

• Gradually reduces GPS precision for hierarchical patches
• Uses pyramid encoder-decoder with Transformers at levels
• Trains via trajectory reconstruction with MSE loss