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多动态时间表示图形卷积网络用于流量预测
Zuojun Wu1, Xiaojun Liu2, Xiaoling Zhang2
1College of Intelligent Manufacturing and Control Engineering, Shandong Institute of Petroleum and Chemical Technology, No. 271 Beier Road, Dongying City, Shandong Province, China. 000221@sdipct.edu.cn.
Scientific reports
|May 14, 2025
概括
本研究介绍了一种新的多动态时间表示图卷积网络 (MDTRGCN),用于在智能交通系统 (ITS) 中更准确地预测交通流量. 该模型有效地捕捉了复杂的时空交通动态,改善了拥堵管理.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 交通流量预测预测
- 图形神经网络的神经网络
背景情况:
- 准确的交通流量预测对于智能交通系统 (ITS) 管理道路拥堵至关重要.
- 现有的方法难以捕捉交通数据中固有的复杂的时空动态.
- 需要先进的模型,可以更好地代表这些动态特征,以提高预测准确度.
研究的目的:
- 提出一个新的多动态时间表示图卷积网络 (MDTRGCN),用于增强流量预测.
- 解决目前利用动态时空交通特征的方法的局限性.
- 为交通网络中的动态空间关系提供可解释的见解.
主要方法:
- 开发了一种动态图形构造方法,以学习路段之间的时空依赖关系.
- 实现了一个动态图形卷积模块,通过动态相邻矩阵汇总节点信息.
- 引入了多方面融合模块,结合了交通量和速度特征.
- 提出了一个时间表示模块,用于推断掩盖的交通数据次序.
主要成果:
- MDTRGCN在真实世界的交通数据集上实现了最先进的性能.
- 与现有方法相比,该模型在流量预测方面表现出更高的准确性.
- 实验结果验证了模型捕捉复杂的时空交通模式的能力.
结论:
- 拟议的MDTRGCN有效地模拟动态时空交通特征,以进行准确的预测.
- 该方法为ITS应用程序的流量预测提供了显著的改进.
- 该模型为道路段的空间关系提供了可解释的见解,有助于交通管理.
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