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Trajectory Similarity Measurement: An Efficiency Perspective

Summary: Reassesses the presumed efficiency advantage of learned trajectory embeddings against direct similarity measures via complexity analysis and experiments. Finds learned methods help mainly with precomputed embeddings; direct measures win for one-off queries, while self-attention models lead learned approaches. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13647
Venue
VLDB
Year
2024
Pagerank
5.7303405e-05
Overall Rank
6,953 | 52.30%
DOI
10.14778/3665844.3665858

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{chang_vldb24,
        title = {{Trajectory Similarity Measurement: An Efficiency Perspective}},
        author = {Chang, Yanchuan and Tanin, Egemen and Cong, Gao and Jensen, Christian S. and Qi, Jianzhong},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {9},
        pages = {2293--2304},
        doi = {10.14778/3665844.3665858},
        url = {https://doi.org/10.14778/3665844.3665858},
        year = {2024}
}

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