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SimRN: Trajectory Similarity Learning in Road Networks based on Distributed Deep Reinforcement Learning

Summary: SimRN, the first DRL-based trajectory-similarity framework for road networks, integrates spatio-temporal prompt extraction, DRL-based trajectory representation with automatic parameter selection and parallel training, plus graph contrastive learning. Yields 20–40% accuracy gains, 2–4× speedups, and strong generalization on tiny training sets via self-supervised contrastive sampling that preserves spatial and temporal constraints. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13859
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,588 | 26.35%
DOI
10.14778/3734839.3734844

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