TrajAgent: An LLM-based Agent Framework for Automated Trajectory Modeling via Collaboration of Large and Small Models

• URL: http://arxiv.org/abs/2410.20445v3
• Date: Fri, 13 Jun 2025 08:02:21 GMT
• Title: TrajAgent: An LLM-based Agent Framework for Automated Trajectory Modeling via Collaboration of Large and Small Models
• Authors: Yuwei Du, Jie Feng, Jie Zhao, Jian Yuan, Yong Li,
• Abstract summary: Trajectory modeling has widespread applications in areas such as life services, urban transportation, and public administration.<n>We propose textitTrajAgent, a framework to facilitate robust and efficient trajectory modeling through automation modeling.
• Score: 10.86175727790196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Trajectory modeling, which includes research on trajectory data pattern mining and future prediction, has widespread applications in areas such as life services, urban transportation, and public administration. Numerous methods have been proposed to address specific problems within trajectory modeling. However, the heterogeneity of data and the diversity of trajectory tasks make effective and reliable trajectory modeling an important yet highly challenging endeavor, even for domain experts. In this paper, we propose \textit{TrajAgent}, a agent framework powered by large language models (LLMs), designed to facilitate robust and efficient trajectory modeling through automation modeling. This framework leverages and optimizes diverse specialized models to address various trajectory modeling tasks across different datasets effectively. In \textit{TrajAgent}, we first develop \textit{UniEnv}, an execution environment with a unified data and model interface, to support the execution and training of various models. Building on \textit{UniEnv}, we introduce an agentic workflow designed for automatic trajectory modeling across various trajectory tasks and data. Furthermore, we introduce collaborative learning schema between LLM-based agents and small speciallized models, to enhance the performance of the whole framework effectively. Extensive experiments on four tasks using four real-world datasets demonstrate the effectiveness of \textit{TrajAgent} in automated trajectory modeling, achieving a performance improvement of 2.38\%-34.96\% over baseline methods.

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