RED: Effective Trajectory Representation Learning with Comprehensive Information
Summary: RED: Transformer MAE for trajectories with road-aware masking, spatio-temporal-user joint embeddings, and attention modified for spatial–temporal correlations. Dual-objective (next-segment prediction + reconstruction) training improves accuracy >5% vs nine SOTA methods across 4 tasks and 3 datasets. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Silin Zhou (University of Electronic Science and Technology of China)
- 2. Shuo Shang (University of Electronic Science and Technology of China)
- 3. Lisi Chen (University of Electronic Science and Technology of China)
- 4. Christian S. Jensen (Aalborg University)
- 5. Panos Kalnis (King Abdullah University of Science and Technology)
BibTeX Citation
@article{zhou_vldb25,
title = {{RED: Effective Trajectory Representation Learning with Comprehensive Information}},
author = {Zhou, Silin and Shang, Shuo and Chen, Lisi and Jensen, Christian S. and Kalnis, Panos},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {2},
pages = {80--92},
doi = {10.14778/3705829.3705830},
url = {https://doi.org/10.14778/3705829.3705830},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 221 | Robust and Fast Similarity Search for Moving Object Trajectories | 2005 | SIGMOD | 0.00024224879 |
| 303 | On The Marriage of Lp-norms and Edit Distance | 2004 | VLDB | 0.00021956234 |
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