TERI: An Effective Framework for Trajectory Recovery with Irregular Time Intervals
Summary: TERI recovers trajectories under irregular sampling without prespecified missing positions, jointly detecting gaps and imputing points. Its RETE Transformer combines learnable Fourier spatiotemporal encoding with transition-pattern and contrastive learning, outperforming baselines. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yile Chen (Nanyang Technological University)
- 2. Gao Cong (Nanyang Technological University)
- 3. Cuauhtemoc Anda (DataSpark)
BibTeX Citation
@article{chen_vldb24,
title = {{TERI: An Effective Framework for Trajectory Recovery with Irregular Time Intervals}},
author = {Chen, Yile and Cong, Gao and Anda, Cuauhtemoc},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {3},
pages = {414--426},
doi = {10.14778/3632093.3632105},
url = {https://doi.org/10.14778/3632093.3632105},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,870 | KAFY: An Extensible and Scalable Transformers-Based System for Trajectory Data Analysis | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,389 | Finding Time Period-Based Most Frequent Path in Big Trajectory Data | 2013 | SIGMOD | 0.00010817927 |
| 3,770 | Calibrating Trajectory Data for Similarity-based Analysis | 2013 | SIGMOD | 7.0297827e-05 |
| 9,688 | Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks | 2022 | VLDB | 5.1408423e-05 |
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