提出低计算开销的路径生成扩散模型，效率提升超80%且性能更优。
Understanding and Mitigating the High Computational Cost in Path Data Diffusion
- 在潜在空间而非图空间进行扩散，降低计算复杂度
- 时间与内存成本分别减少82.8%和83.1%，性能反超现先进方法24.5%~34.0%
- 适合需要高效生成城市路径数据的智能交通系统应用
移动服务、导航系统与智能交通技术的发展使得大规模路径数据收集成为可能。建模此类路径数据分布（即路径生成问题）对理解城市出行模式及构建智能交通系统至关重要。近期研究尝试使用扩散模型解决该问题，因其能捕捉多模态分布并支持条件生成。一项最新工作在图空间显式设计扩散过程，取得了当前最优性能，但其时间和内存开销过高，难以实用。本文从理论与实验两方面分析该方法，发现高开销主因在于图空间的显式扩散设计。为此，我们提出潜在空间路径扩散（LPD）模型，将扩散过程转移到潜在空间。实验表明，该方法在时间与内存上分别降低82.8%与83.1%，同时性能优于现有方法，在多数场景提升24.5%~34.0%。
原文摘要 · Abstract (English)
Advancements in mobility services, navigation systems, and smart transportation technologies have made it possible to collect large amounts of path data. Modeling the distribution of this path data, known as the Path Generation (PG) problem, is crucial for understanding urban mobility patterns and developing intelligent transportation systems. Recent studies have explored using diffusion models to address the PG problem due to their ability to capture multimodal distributions and support conditional generation. A recent work devises a diffusion process explicitly in graph space and achieves state-of-the-art performance. However, this method suffers a high computation cost in terms of both time and memory, which prohibits its application. In this paper, we analyze this method both theoretically and experimentally and find that the main culprit of its high computation cost is its explicit design of the diffusion process in graph space. To improve efficiency, we devise a Latent-space Path Diffusion (LPD) model, which operates in latent space instead of graph space. Our LPD significantly reduces both time and memory costs by up to 82.8% and 83.1%, respectively. Despite these reductions, our approach does not suffer from performance degradation. It outperforms the state-of-the-art method in most scenarios by 24.5%~34.0%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。