Blurred Encoding for Trajectory Representation Learning

• URL: http://arxiv.org/abs/2511.13741v1
• Date: Wed, 12 Nov 2025 03:51:57 GMT
• Title: Blurred Encoding for Trajectory Representation Learning
• Authors: Silin Zhou, Yao Chen, Shuo Shang, Lisi Chen, Bingsheng He, Ryosuke Shibasaki,
• Abstract summary: Tray representation learning (TRL) maps trajectories to vector embeddings and facilitates tasks such as trajectory classification and similarity search.<n>State-of-the-art (SOTA) TRL methods transform raw GPS trajectories to grid or road trajectories to capture high-level travel semantics.<n>But they lose fine-grained spatial-temporal details as multiple GPS points are grouped into a single grid cell or road segment.<n>We propose the BLUrred jector method, dubbed BLUE, which gradually reduces the precision of GPS coordinates to create hierarchical patches.
• Score: 56.642548506419
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• LitePT: Lighter Yet Stronger Point Transformer [50.6430530112838]
  We analyse the role of different computational blocks in 3D point cloud networks.<n>We propose a new, improved 3D point cloud backbone that employs convolutions in early stages and switches to attention for deeper layers.<n>The resulting LitePT model has $3.6times$ fewer parameters, runs $2times$ faster, and uses $2times$ less memory than the state-of-the-art Point Transformer V3.
  arXiv  Detail & Related papers  (2025-12-15T18:59:57Z)
• R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization [66.87005863868181]
  We introduce a covisibility graph-based global encoding learning and data augmentation strategy.<n>We revisit the network architecture and local feature extraction module.<n>Our method achieves state-of-the-art on challenging large-scale datasets without relying on network ensembles or 3D supervision.
  arXiv  Detail & Related papers  (2025-01-02T18:59:08Z)
• RED: Effective Trajectory Representation Learning with Comprehensive Information [41.58502888707689]
  Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks.<n>We propose a self-supervised TRL framework, called RED, which effectively exploits multiple types of trajectory information.
  arXiv  Detail & Related papers  (2024-11-22T17:51:21Z)
• Grid and Road Expressions Are Complementary for Trajectory Representation Learning [40.94269411061165]
  Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks. Existing TRL methods use either grid trajectories, capturing movement in free space, or road trajectories, capturing movement in a road network, as input. We propose a novel multimodal TRL method, dubbed GREEN, to jointly utilize Grid and Road trajectory Expressions for Effective representatioN learning.
  arXiv  Detail & Related papers  (2024-11-22T07:15:46Z)
• NLP-enabled Trajectory Map-matching in Urban Road Networks using a Transformer-based Encoder-decoder [1.3812010983144802]
  This study introduces a data-driven, deep learning-based map-matching framework, formulating the task as machine translation, inspired by NLP.<n>A transformer-based encoder-decoder model learns contextual representations of noisy GPS points to infer trajectory behavior and road structures in an end-to-end manner.<n>Experiments on synthetic trajectories show that this approach outperforms conventional methods by integrating contextual awareness.
  arXiv  Detail & Related papers  (2024-04-18T18:39:23Z)
• SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation [91.91385816767057]
  Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. We propose a simple encoder-decoder, named SED, for open-vocabulary semantic segmentation. Our SED method achieves mIoU score of 31.6% on ADE20K with 150 categories at 82 millisecond ($ms$) per image on a single A6000.
  arXiv  Detail & Related papers  (2023-11-27T05:00:38Z)
• DETR Doesn't Need Multi-Scale or Locality Design [69.56292005230185]
  This paper presents an improved DETR detector that maintains a "plain" nature. It uses a single-scale feature map and global cross-attention calculations without specific locality constraints. We show that two simple technologies are surprisingly effective within a plain design to compensate for the lack of multi-scale feature maps and locality constraints.
  arXiv  Detail & Related papers  (2023-08-03T17:59:04Z)
• RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer [15.350300338463969]
  We propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It then introduces a Sub-Graph Generation module to represent each GPS point as a sub-graph structure of the road network around the GPS point.
  arXiv  Detail & Related papers  (2022-11-23T11:28:32Z)
• Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection [3.093890460224435]
  Most GPS trajectory datasets include stay-points irregularity, which makes map-matching algorithms mismatch trajectories to irrelevant streets. We cluster stay-points in a trajectory dataset with DBSCAN and eliminate redundant data to improve the efficiency of the map-matching algorithm. Our approach yields 27.39% data size reduction and 8.9% processing time reduction with the same accurate results as the previous fuzzy-logic based map-matching approach.
  arXiv  Detail & Related papers  (2022-08-04T20:41:13Z)
• Progressive Coordinate Transforms for Monocular 3D Object Detection [52.00071336733109]
  We propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations. In this paper, we propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations.
  arXiv  Detail & Related papers  (2021-08-12T15:22:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.