arXiv AI June 17, 2026 By Ruixin Song, Md Mahbub Alam, Zahra Sadeghi, Amilcar Soares, Jos\'e F. Rodrigues-Jr, Gabriel Spadon

MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories

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arXiv:2606. 17978v1 Announce Type: new Abstract: Trajectory similarity is a fundamental task in analyzing mobility patterns, essential for applications such as route pattern extraction, mobility prediction, and anomaly detection.

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arXiv Machine Learning

Sep 7

TSMini: A Simple Yet Highly Effective Trajectory Similarity Learning Model

TSMini is a trajectory similarity learning model that introduces a sub-view modeling mechanism and a k‑nearest‑neighbor‑based loss to capture multi‑granularity trajectory patterns and relative similarity ranks. These innovations allow TSMini to approximate conventional trajectory similarity measures with high accuracy. Experiments demonstrate that TSMini outperforms state‑of‑the‑art models by an average of 15% on widely used similarity metrics.

By Yanchuan Chang, Dingyang Lyu, Xu Cai, Christian S. Jensen, Jianzhong Qi

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arXiv AI

Sep 10

DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning -- Extended Version

DGCPath is a Distribution‑Aware Generative Contrastive framework designed for self‑supervised path representation learning. It combines a diffusion‑based view generator, a variational contrastive mechanism that aligns latent features at the distribution level, and a generative cross‑supervision module for view‑level consistency. Experiments on three real‑world trajectory datasets show that DGCPath surpasses state‑of‑the‑art baselines on two downstream tasks, indicating stronger generalization and representation effectiveness.

By Sean Bin Yang, Hao Miao, Zongyi Xu, Jilin Hu, Xiangmeng Wang, Hua Lu, Bin Yang, Christian S. Jensen

rag diffusion benchmarks safety

arXiv Machine Learning

Aug 4

Using Lower-Bound Representations for Trajectory Similarity Learning

arXiv:2608. 01039v1 Announce Type: cross Abstract: Trajectory similarity learning is fundamental to efficient trajectory retrieval under complex distance measures.

By Liwei Deng, Haotian Meng, Yupu Zhang, Yan Zhao, Torben Bach Pedersen, Kai Zheng, Christian S. Jensen

rag benchmarks

arXiv Machine Learning

Jul 1

Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning

arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.

By Ji Cao, Yu Wang, Tongya Zheng, Jie Song, Qinghong Guo, Zujie Ren, Canghong Jin, Gang Chen, Mingli Song

llms rag benchmarks

arXiv AI

Jul 22

SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction

arXiv:2601. 18537v4 Announce Type: replace-cross Abstract: Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and environmental factors.

By Linyong Gan, Zimo Li, Wenxin Xu, Xingjian Li, Jianhua Z. Huang, Enmei Tu, Shuhang Chen

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arXiv AI

Jun 6

AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks

arXiv:2606. 06311v1 Announce Type: new Abstract: Accurate vessel trajectory prediction is essential for safe and efficient maritime operations, enabling collision avoidance and supporting route optimization.

By Wonmo Koo, Sanha Chang, Heeyoung Kim

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TSMini: A Simple Yet Highly Effective Trajectory Similarity Learning Model

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