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A Deep Generative Model for Trajectory Modeling and Utilization

Summary: A differentially private deep generative model synthesizes trajectories from map-matched road sequences, exploiting road properties, spatial correlations, and periodicity to capture skewed spatio-temporal distributions. Meta-learning enables stepwise generation and direct downstream use without processing raw trajectories. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13535
Venue
VLDB
Year
2023
Pagerank
5.8364579e-05
Overall Rank
6,579 | 54.87%
DOI
10.14778/3574245.3574277

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb23,
        title = {{A Deep Generative Model for Trajectory Modeling and Utilization}},
        author = {Wang, Yong and Li, Guoliang and Li, Kaiyu and Yuan, Haitao},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {4},
        pages = {973--985},
        doi = {10.14778/3574245.3574277},
        url = {https://doi.org/10.14778/3574245.3574277},
        year = {2023}
}

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