InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories

TL;DR

InsTraj is a novel framework that uses diffusion models guided by natural language instructions to generate realistic, diverse, and semantically faithful GPS trajectories for urban applications.

Contribution

It introduces a multimodal diffusion transformer guided by large language models to interpret natural language travel intentions and generate high-fidelity trajectories.

Findings

Outperforms state-of-the-art methods in realism and diversity.

Generates trajectories faithful to user instructions.

Effectively interprets complex natural language travel intents.

Abstract

The generation of realistic and controllable GPS trajectories is a fundamental task for applications in urban planning, mobility simulation, and privacy-preserving data sharing. However, existing methods face a two-fold challenge: they lack the deep semantic understanding to interpret complex user travel intent, and struggle to handle complex constraints while maintaining the realistic diversity inherent in human behavior. To resolve this, we introduce InsTraj, a novel framework that instructs diffusion models to generate high-fidelity trajectories directly from natural language descriptions. Specifically, InsTraj first utilizes a powerful large language model to decipher unstructured travel intentions formed in natural language, thereby creating rich semantic blueprints and bridging the representation gap between intentions and trajectories. Subsequently, we proposed a multimodal trajectory diffusion transformer that can integrate semantic guidance to generate high-fidelity and instruction-faithful trajectories that adhere to fine-grained user intent. Comprehensive experiments on real-world datasets demonstrate that InsTraj significantly outperforms state-of-the-art methods in generating trajectories that are realistic, diverse, and semantically faithful to the input instructions.