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Given the sequential nature of trajectory data, previous efforts have been primarily devoted to the utilization of RNNs or Transformers.<\/jats:p>\n          <jats:p>In this paper, we argue that the common practice of treating trajectory as sequential data results in excessive attention to capturing long-term global dependency between two sequences. Instead, our investigation reveals the pivotal role of local similarity, prompting a revisit of simple CNNs for trajectory similarity learning. We introduce ConvTraj, incorporating both 1D and 2D convolutions to capture sequential and geo-distribution features of trajectories, respectively. In addition, we conduct a series of theoretical analyses to justify the effectiveness of ConvTraj. Experimental results on four real-world large-scale datasets demonstrate that ConvTraj achieves state-of-the-art accuracy in trajectory similarity search. Owing to the simple network structure of ConvTraj, the training and inference speed on the Porto dataset with 1.6 million trajectories are increased by at least 240x and 2.16x, respectively.<\/jats:p>","DOI":"10.14778\/3717755.3717762","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T15:51:49Z","timestamp":1747756309000},"page":"1013-1021","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Revisiting CNNs for Trajectory Similarity Learning"],"prefix":"10.14778","volume":"18","author":[{"given":"Zhihao","family":"Chang","sequence":"first","affiliation":[{"name":"School of Software Technology, Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linzhu","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Li","sequence":"additional","affiliation":[{"name":"Hangzhou High-Tech Zone (Binjiang), Institute of Blockchain and Data Security and Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sai","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hangzhou High-Tech Zone (Binjiang), Institute of Blockchain and Data Security and Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781315373515"},{"key":"e_1_2_1_2_1","volume-title":"Swami","author":"Agrawal Rakesh","year":"1993","unstructured":"Rakesh Agrawal, Christos Faloutsos, and Arun N. 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