融合聚类与傅里叶Mamba，提升交通流量预测精度
DKGCM: A Spatio-Temporal Prediction Model for Traffic Flow by Fusing Spatial Node Clustering Method and Fourier Bidirectional Mamba Mechanism
- 用动态时间规整与聚类分组节点，捕捉空间依赖关系
- 结合快速傅里叶与双向Mamba，有效建模时间序列特征
- 在三个公开数据集上优于主流方法，适合交通管理应用
精准的交通需求预测有助于交通管理部门更高效地调配资源，提升利用效率。然而，交通系统中复杂的时空关系仍制约着预测模型的表现。为提升时空交通需求预测的准确性，本文提出一种新型图卷积网络结构DKGCM。首先，基于不同交通节点的流量分布特性，提出一种基于时序相似性的聚类图卷积方法DK-GCN，利用动态时间规整（DTW）与K-means聚类对交通节点进行分组，更有效地捕捉空间依赖。在时间维度上，将快速傅里叶变换（FFT）融入双向Mamba深度学习框架，以捕获交通需求的时间依赖性。为进一步优化模型训练，引入GRPO强化学习策略，增强损失函数的反馈机制。大量实验表明，该模型在三个公开数据集上均优于多种先进方法，表现优异。
原文摘要 · Abstract (English)
Accurate traffic demand forecasting enables transportation management departments to allocate resources more effectively, thereby improving their utilization efficiency. However, complex spatiotemporal relationships in traffic systems continue to limit the performance of demand forecasting models. To improve the accuracy of spatiotemporal traffic demand prediction, we propose a new graph convolutional network structure called DKGCM. Specifically, we first consider the spatial flow distribution of different traffic nodes and propose a novel temporal similarity-based clustering graph convolution method, DK-GCN. This method utilizes Dynamic Time Warping (DTW) and K-means clustering to group traffic nodes and more effectively capture spatial dependencies. On the temporal scale, we integrate the Fast Fourier Transform (FFT) within the bidirectional Mamba deep learning framework to capture temporal dependencies in traffic demand. To further optimize model training, we incorporate the GRPO reinforcement learning strategy to enhance the loss function feedback mechanism. Extensive experiments demonstrate that our model outperforms several advanced methods and achieves strong results on three public datasets.
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