基于变量模态分解和深度学习的短期交通流量预测的组合模型
Chuanxiang Ren1, Fangfang Fu1, Changchang Yin2
1College of Transportation, Shandong University of Science and Technology, Qingdao, 266590, China.
Scientific reports
|May 17, 2025
概括
这项研究引入了一种新的VMD-GAT-MGTCN模型,用于准确的短期流量预测. 该模型有效地处理流量流动的波动性,优于现有的方法,特别是在突变区域.
科学领域:
- 人工智能的人工智能
- 运输工程 运输工程
- 数据科学数据科学数据科学
背景情况:
- 深度学习模型在流量预测方面面临挑战,原因是固有的非线性,不稳定性和波动性.
- 准确的短期交通流量预测对于智能交通系统至关重要.
研究的目的:
- 提出一种新的综合预测模型VMD-GAT-MGTCN,以提高短期流量预测的准确性.
- 为了应对交通流动不确定性和波动性所带来的挑战.
主要方法:
- 变量模态分解 (VMD) 用于将流量数据分解为模态组件.
- 开发了一个时空特征模型,集成图表注意网络 (GAT) 和多门注意时间卷积网络 (MGTCN).
- 预测的模式组件被堆叠在一起,以生成最终的流量预测.
主要成果:
- 与基线和其他模型相比,拟议的VMD-GAT-MGTCN模型显示出更高的预测准确性和有效性.
- 变量模态分解显著提高了VMD-GAT-MGTCN模型的预测性能.
- 该模型取得了良好的预测结果,特别是在流量变化的区域.
结论:
- VMD-GAT-MGTCN模型为准确的短期流量预测提供了一个强大的解决方案.
- VMD,GAT和MGTCN的整合有效地捕捉了时空特征,并处理了流量动态.
- 这种方法显示了改善智能交通系统的巨大潜力.
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