Academic

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

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. 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 seman

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Yuanshao Zhu, Yuxuan Liang, Xiangyu Zhao, Liang Han, Xinwei Fang, Xuetao Wei, James Jianqiao Yu · · 1 min read · 6 views
#cs.AI

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. 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.

Executive Summary

The article InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories proposes a novel framework that utilizes large language models to decipher travel intentions from natural language descriptions and a multimodal trajectory diffusion transformer to generate high-fidelity and instruction-faithful trajectories. The InsTraj framework addresses the challenges of existing methods by providing a deep semantic understanding of user travel intent and handling complex constraints while maintaining realistic diversity. Comprehensive experiments demonstrate that InsTraj significantly outperforms state-of-the-art methods in generating realistic, diverse, and semantically faithful trajectories. The proposed framework has the potential to revolutionize urban planning, mobility simulation, and privacy-preserving data sharing applications.

Key Points

  • ▸ InsTraj utilizes a powerful large language model to decipher travel intentions from natural language descriptions.
  • ▸ The framework employs a multimodal trajectory diffusion transformer to generate high-fidelity and instruction-faithful trajectories.
  • ▸ Comprehensive experiments demonstrate that InsTraj significantly outperforms state-of-the-art methods.

Merits

Strength

The proposed framework addresses the challenges of existing methods by providing a deep semantic understanding of user travel intent and handling complex constraints while maintaining realistic diversity.

Methodological innovation

The InsTraj framework utilizes a novel combination of large language models and multimodal trajectory diffusion transformers to generate high-fidelity and instruction-faithful trajectories.

Empirical validation

The article presents comprehensive experiments that demonstrate the effectiveness of the InsTraj framework in generating realistic, diverse, and semantically faithful trajectories.

Demerits

Limitation

The framework relies on the availability of large language models and multimodal trajectory diffusion transformers, which may be computationally intensive and resource-constrained.

Scalability

The framework may not be scalable to large datasets or complex scenarios, which could limit its practical applications.

Interpretability

The article does not provide a thorough analysis of the interpretability of the generated trajectories, which could be a concern in applications where explainability is critical.

Expert Commentary

The InsTraj framework represents a significant innovation in the field of trajectory generation, and its potential applications are vast and varied. The framework's ability to provide a deep semantic understanding of user travel intent and handle complex constraints while maintaining realistic diversity is a major strength. However, the framework's reliance on large language models and multimodal trajectory diffusion transformers, as well as its potential scalability limitations, are concerns that need to be addressed. Furthermore, the article could benefit from a more thorough analysis of the interpretability of the generated trajectories. Overall, the InsTraj framework is a promising development that has the potential to revolutionize urban planning, mobility simulation, and privacy-preserving data sharing applications.

Recommendations

  • ✓ Future research should focus on addressing the scalability limitations of the InsTraj framework and developing more efficient and interpretable methods for generating high-fidelity and instruction-faithful trajectories.
  • ✓ The InsTraj framework should be applied to real-world scenarios to evaluate its effectiveness and identify areas for improvement.

Sources

Original: arXiv - cs.AI

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