InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories
This addresses the challenge of generating realistic and semantically faithful trajectories for applications in urban planning, mobility simulation, and privacy-preserving data sharing, representing a novel method for a known bottleneck.
The authors tackled the problem of generating realistic and controllable GPS trajectories by introducing InsTraj, a framework that uses large language models and diffusion models to interpret natural language travel intentions and produce high-fidelity trajectories, significantly outperforming state-of-the-art methods in experiments on real-world datasets.
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.