NeurIPS 2025

TrajAgent: An LLM-Agent Framework for Trajectory
Modeling via Large-and-Small Model Collaboration
Yuwei Du∗, Jie Feng∗†, Jie Zhao, Yong Li†
Department of Electronic Engineering, BRNist, Tsinghua University, Beijing, China
{fengjie,liyong07}@tsinghua.edu.cn
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 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 TrajAgent, we first develop UniEnv, an execution
environment with a unified data and model interface, to support the execution
and training of various models. Building on 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 TrajAgent in automated
trajectory modeling, achieving a performance improvement of 2.38%-69.91% over
baseline methods. The codes and data can be accessed via https://github.
com/tsinghua-fib-lab/TrajAgent.
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Introduction
With the rapid development of web services and mobile devices [68, 6], large-scale trajectory data,
suc...