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预培训改进的时空图形网络用于通用化,提高交通预测的性能

Xiangyue Zhang¹, Chao Li², Ling Ji³

¹School of Information Science and Engineering, Linyi University, Linyi, 276000, China.

Scientific reports

|July 29, 2025

概括

本研究引入了一种增强的预训练方法,即改进的时空扩散图 (ImPreSTDG),以改进交通预测模型. ImPreSTDG有效地捕捉了长期的时空依赖性,并降低了计算成本,在现实世界数据集上表现优于现有的方法.

科学领域:

* 人工智能 * 人工智能
* 智慧城市发展
* * 交通工程交通工程

背景情况:

*现有的流量预测模型,通常基于图形卷积网络 (GCNs),与长期的时空依赖和高计算成本作斗争.
*为新数据集重新训练这些模型减少了准确性并增加了时间投资.
*复杂的模块提高了性能,但增加了计算需求.

研究的目的:

*为交通预测提出一个改进的预训练方法,即改进的时空扩散图 (ImPreSTDG).
* 解决当前模型中捕捉长期时空依赖性和高计算成本的局限性.
* 提高模型概括能力,减少再培训的开销.

主要方法:

*将Denoised Diffusion Probability Model (DDPM) 集成到预培训过程中,以增强从长期数据中学习,并减少计算负载.
* 在预培训期间实施数据掩盖和恢复策略,DDPM重建掩盖的段落.
* 加入了使用选择性状态空间模型 (SSM) 的Mamba模块,以高效处理长序列并捕获多变量时空相关性.

主要成果:

* 拟议的ImPreSTDG方法显著提高了模型处理长期时空依赖性的能力.
*该方法有效地解决了与缺失数据和高计算成本相关的挑战.
*在三个真实世界交通数据集上的实验验证了预训练方法的提高效率和准确性.

结论:

* ImPreSTDG预训练方法为交通预测提供了更有效,更准确的解决方案,特别是对于长期依赖.
* 该研究表明,在不影响预测准确性的情况下,计算成本显著降低.
*这种增强的方法为智能城市交通管理系统提供了强大的框架.

关键词:

DDPMM DDPM是指一个人.预培训预培训预培训时间空间图形网络.空间模型的空间状态模型预测交通情况.

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