DiffPath: Generating Road Network based Path with Latent Diffusion Model

Yiwen Fan, Yan Lin, haichen wang, Hongfan Gao, Ronghui Xu, Jilin Hu

Keywords: Path Generation, Latent Diffusion Model, Path Distribution, Long-range Dependencies

Abstract:

With the increasing use of GPS technology, path has become essential for applications such as navigation, urban planning, and traffic optimization. However, obtaining real-world path presents challenges due to privacy concerns and the difficulty of collecting large datasets. Existing methods, including count-based and deep learning approaches, struggle with two main challenges: handling complex distributions of path segments and ensuring global coherence in generated paths. To address these, we introduce DiffPath, a path generation model based on Latent Diffusion Models (LDMs). By embedding path into a continuous latent space and leveraging a transformer architecture, DiffPath captures both local transitions and global dependencies, ensuring the generation of realistic paths. Experimental results demonstrate that our model outperforms existing approaches in generating paths that adhere to real-world road network structures while maintaining privacy.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 3621