DeepTEA: Effective and Efficient Online Time-dependent Trajectory Outlier Detection
Summary: DeepTEA models time- and location-varying traffic with deep probabilistic learning for trajectory outlier detection. A fast approximate variant enables online, real-time analysis at million-trajectory scale while improving accuracy over seven baselines by 17.52%. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaolin Han (University of Hong Kong)
- 2. Reynold Cheng (University of Hong Kong)
- 3. Chenhao Ma (University of Hong Kong)
- 4. Tobias Grubenmann (University of Bonn)
BibTeX Citation
@article{han_vldb22,
title = {{DeepTEA: Effective and Efficient Online Time-dependent Trajectory Outlier Detection}},
author = {Han, Xiaolin and Cheng, Reynold and Ma, Chenhao and Grubenmann, Tobias},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {7},
pages = {1493--1505},
doi = {10.14778/3523210.3523225},
url = {https://doi.org/10.14778/3523210.3523225},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,064 | Finding Locally Densest Subgraphs: A Convex Programming Approach | 2022 | VLDB | 7.6898682e-05 |
| 4,923 | A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery | 2022 | SIGMOD | 6.3522796e-05 |
| 7,902 | Origin-Destination Travel Time Oracle for Map-based Services | 2023 | SIGMOD | 5.4297829e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,021 | Distributed Trajectory Similarity Search | 2017 | VLDB | 9.1719539e-05 |
| 3,789 | Efficient Algorithms for Densest Subgraph Discovery on Large Directed Graphs | 2020 | SIGMOD | 7.0191274e-05 |
| 4,923 | A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery | 2022 | SIGMOD | 6.3522796e-05 |
| 11,996 | On Analyzing Graphs with Motif-Paths | 2021 | VLDB | 4.9793485e-05 |
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