TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories

TL;DR

TrajCogn leverages large language models with novel embedding and prompting techniques to accurately analyze movement patterns and travel purposes from trajectories across multiple tasks.

Contribution

The paper introduces TrajCogn, a novel trajectory learning framework that adapts LLMs for spatio-temporal data analysis by designing specialized embeddings and prompts.

Findings

TrajCogn outperforms baseline models on real-world datasets.

It effectively extracts movement patterns and travel purposes.

The approach demonstrates high versatility across different tasks.

Abstract

Spatio-temporal trajectories are crucial in various data mining tasks. It is important to develop a versatile trajectory learning method that performs different tasks with high accuracy. This involves effectively extracting two core aspects of information--movement patterns and travel purposes--from trajectories. However, this is challenging due to limitations in model capacity and the quality and scale of trajectory datasets. Meanwhile, large language models (LLMs) have shown great success in versatility by training on large-scale, high-quality datasets. Given the similarities between trajectories and sentences, there's potential to leverage LLMs to develop an effective trajectory learning method. However, standard LLMs are not designed to handle the unique spatio-temporal features of trajectories and cannot extract movement patterns and travel purposes. To address these challenges, we propose a model called TrajCogn that effectively utilizes LLMs to model trajectories. TrajCogn leverages the strengths of LLMs to create a versatile trajectory learning approach while addressing the limitations of standard LLMs. First, TrajCogn incorporates a novel trajectory semantic embedder that enables LLMs to process spatio-temporal features and extract movement patterns and travel purposes. Second, TrajCogn introduces a new trajectory prompt that integrates these patterns and purposes into LLMs, allowing the model to adapt to various tasks. Extensive experiments on two real-world datasets and two representative tasks demonstrate that TrajCogn successfully achieves its design goals. Codes are available at https://anonymous.4open.science/r/TrajCogn-5021.