InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories
Summary: arXiv:2604.04106v1 Announce Type: new
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.
Introduction
InsTraj addresses the pressing need for improved trajectory generation by leveraging advanced natural language processing capabilities. Traditional methods often fail to capture the nuanced nature of user intentions, resulting in trajectories that may lack realism or relevance.
Methodology
InsTraj operates through a two-step process:
- Semantic Interpretation: The framework first employs a powerful large language model to decipher unstructured travel intentions articulated in natural language. This step is crucial as it creates rich semantic blueprints that effectively bridge the representation gap between user intentions and GPS trajectories.
- Trajectory Generation: Following semantic interpretation, InsTraj utilizes a multimodal trajectory diffusion transformer. This innovative model integrates the derived semantic guidance to generate high-fidelity trajectories that remain faithful to the user’s instructions and preferences.
Key Features
InsTraj stands out due to several key features:
- High-Fidelity Outputs: The generated trajectories are not only realistic but also exhibit a high degree of detail that aligns with user intentions.
- Semantic Faithfulness: The framework ensures that the trajectories adhere closely to the fine-grained user intent, addressing a common shortfall in existing methods.
- Diversity in Trajectories: InsTraj maintains the inherent diversity found in human travel patterns, allowing for a wide range of realistic outputs.
Experimental Results
Comprehensive experiments conducted on various real-world datasets reveal that InsTraj significantly outperforms state-of-the-art methods in trajectory generation. The results highlight improvements in both realism and semantic alignment when compared to existing solutions.
Conclusion
InsTraj represents a significant advancement in the field of trajectory generation, effectively addressing the limitations of previous methodologies. By incorporating deep semantic understanding and advanced diffusion models, InsTraj not only facilitates realistic trajectory generation but also enhances the relevance and applicability of generated data for urban planning, mobility simulation, and privacy-preserving data sharing.
Future Work
Future research will focus on further refining the model’s capabilities and exploring its applications in various domains, including autonomous navigation and personalized mobility solutions.