DeepTEA: Effective and Efficient Online Time-dependent Trajectory Outlier Detection
Summary: DeepTEA is a deep-probabilistic time-dependent trajectory anomaly detector, capturing traffic-driven outliers. It enables real-time approximation and scales to millions of trajectories, achieving 17.52% higher accuracy than seven baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaolin Han
- 2. Reynold Cheng
- 3. Chenhao Ma
- 4. Tobias Grubenmann
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,575 | Finding Locally Densest Subgraphs: A Convex Programming Approach | 2022 | VLDB | 6.9528126e-05 |
| 5,265 | A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery | 2022 | SIGMOD | 5.5972878e-05 |
| 8,284 | Origin-Destination Travel Time Oracle for Map-based Services | 2023 | SIGMOD | 4.5435639e-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 |
|---|---|---|---|---|
| 1,776 | Distributed Trajectory Similarity Search | 2017 | VLDB | 0.00010593716 |
| 4,344 | Efficient Algorithms for Densest Subgraph Discovery on Large Directed Graphs | 2020 | SIGMOD | 6.2744553e-05 |
| 5,265 | A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery | 2022 | SIGMOD | 5.5972878e-05 |
| 11,492 | On Analyzing Graphs with Motif-Paths | 2021 | VLDB | 4.1945683e-05 |
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