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Fast Large-Scale Trajectory Clustering

Summary: Introduces k-paths: clustering large-scale road-network trajectories into k representative paths with minimal data-dependent tuning. Map matching, a compact intermediate representation, and an edge-based distance enable clustering millions of taxi trips in under a minute—up to 100× faster than prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12346
Venue
VLDB
Year
2020
Pagerank
6.3596697e-05
Overall Rank
5,112 | 64.93%
DOI
10.14778/3357377.3357380

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb20,
        title = {{Fast Large-Scale Trajectory Clustering}},
        author = {Wang, Sheng and Bao, Zhifeng and Culpepper, J. Shane and Sellis, Timos and Qin, Xiaolin},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {1},
        pages = {29--42},
        doi = {10.14778/3357377.3357380},
        url = {https://doi.org/10.14778/3357377.3357380},
        year = {2020}
}

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