标题： DKGCM：一种通过融合空间节点聚类方法和傅里叶双向马姆巴机制的时空交通流预测模型 显示英文标题

标题： DKGCM: A Spatio-Temporal Prediction Model for Traffic Flow by Fusing Spatial Node Clustering Method and Fourier Bidirectional Mamba Mechanism

摘要： 准确的交通需求预测使交通管理部门能够更有效地分配资源，从而提高其利用效率。 然而，交通系统中的复杂时空关系仍然限制了需求预测模型的性能。 为了提高时空交通需求预测的准确性，我们提出了一种新的图卷积网络结构，称为DKGCM。 具体来说，我们首先考虑不同交通节点的空间流量分布，并提出一种基于时间相似性的聚类图卷积方法， DK-GCN。 该方法利用动态时间规整（DTW）和K均值聚类对交通节点进行分组，更有效地捕捉空间依赖性。 在时间尺度上，我们在双向Mamba深度学习框架中集成快速傅里叶变换（FFT），以捕捉交通需求中的时间依赖性。 为了进一步优化模型训练，我们引入GRPO强化学习策略，以增强损失函数反馈机制。 大量实验表明，我们的模型优于几种先进方法，并在三个公开数据集上取得了强劲的结果。 显示英文摘要

摘要： 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.