用扩散模型提升交通流量矩阵估计精度
Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model
- 用去噪扩散模型学习交通矩阵分布，结合降维预处理
- 通过参数化噪声因子优化，实现更精准的流量估计
- 在真实数据集上优于现有方法，适合交通规划研究
交通矩阵估计（TME）问题已研究数十年。深度生成模型的进展为该问题提供了新思路。本文首次将去噪扩散概率模型（DDPM）用于TME，利用其强大的分布建模能力。为提升DDPM在交通矩阵分布学习中的表现，设计了降维预处理模块，在保留各起点-终点流多样性的同时降低维度。为进一步提高估计精度，对DDPM中的噪声因子进行参数化，并将TME转化为梯度下降优化问题。在两个真实世界交通矩阵数据集上的实验表明，所提方法在矩阵合成与估计任务上均显著优于现有先进方法。
原文摘要 · Abstract (English)
The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ability of denoising diffusion probabilistic models (DDPMs) on distribution learning, and for the first time adopt DDPM to address the TME problem. To ensure a good performance of DDPM on learning the distributions of TMs, we design a preprocessing module to reduce the dimensions of TMs while keeping the data variety of each OD flow. To improve the estimation accuracy, we parameterize the noise factors in DDPM and transform the TME problem into a gradient-descent optimization problem. Finally, we compared our method with the state-of-the-art TME methods using two real-world TM datasets, the experimental results strongly demonstrate the superiority of our method on both TM synthesis and TM estimation.
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