Revisiting CNNs for Trajectory Similarity Learning

Summary: Revisits CNNs for trajectories, arguing local similarity matters more than long-range dependency and proposing ConvTraj with 1D convs for sequential patterns and 2D convs for geo-distribution. With theoretical support, ConvTraj achieves SOTA accuracy and large speedups (240× training, 2.16× inference on 1.6M Porto trajectories). (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13775
Venue: VLDB
Year: 2025
Pagerank: 4.1905499e-05
Overall Rank: 10,538 | 26.77%
DOI: 10.14778/3717755.3717762

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
251	Robust and Fast Similarity Search for Moving Object Trajectories	2005	SIGMOD	0.00030589775
358	On The Marriage of Lp-norms and Edit Distance	2004	VLDB	0.00026059337
1,749	Distributed Trajectory Similarity Search	2017	VLDB	0.00010684148
2,198	DITA: Distributed In-Memory Trajectory Analytics	2018	SIGMOD	9.3096071e-05
6,508	Trajectory Similarity Measurement: An Efficiency Perspective	2024	VLDB	5.0273291e-05

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