Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter
Summary: Neural travel-time model aligns each OD query with its historical trajectory in a shared latent space, exploiting road-segment embeddings and time-slot embeddings. A temporal graph captures weekly/daily periodicity; at inference only OD input is needed to generate travel time. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Haitao Yuan (Tsinghua University)
- 2. Guoliang Li (Tsinghua University)
- 3. Zhifeng Bao (Royal Melbourne Institute of Technology)
- 4. Ling Feng (Tsinghua University)
BibTeX Citation
@inproceedings{yuan_sigmod20,
title = {{Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter}},
author = {Yuan, Haitao and Li, Guoliang and Bao, Zhifeng and Feng, Ling},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389771},
url = {https://dl.acm.org/doi/10.1145/3318464.3389771},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 17 of 17 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,177 | On Map-Matching Vehicle Tracking Data | 2005 | VLDB | 0.000116417 |
| 1,389 | Finding Time Period-Based Most Frequent Path in Big Trajectory Data | 2013 | SIGMOD | 0.00010817927 |
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