TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories
Zeyu Zhou, Yan Lin, Haomin Wen, Shengnan Guo, Jilin Hu, Youfang Lin, Huaiyu Wan
Abstract
Spatio-temporal trajectories are crucial for data mining tasks, requiring versatile learning methods that can accurately extract movement patterns and travel purposes. While large language models (LLMs) have shown remarkable versatility through training on extensive datasets, and trajectories share similarities with natural language, standard LLMs cannot directly handle spatio-temporal features or extract trajectory-specific information. We propose TrajCogn, a model that effectively adapts LLMs for trajectory learning. TrajCogn incorporates a novel trajectory semantic embedder to process spatio-temporal features and extract movement patterns and travel purposes, along with a trajectory prompt that integrates this information into LLMs for various downstream tasks. Experiments on three real-world datasets and four representative tasks demonstrate TrajCogn's effectiveness.
BibTeX
@inproceedings{ijcai2025_trajcognleveragi,
title = {TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories},
author = {Zeyu Zhou and Yan Lin and Haomin Wen and Shengnan Guo and Jilin Hu and Youfang Lin and Huaiyu Wan},
booktitle = {IJCAI 2025},
year = {2025}
}