基于自适应动态关联矩阵的时空一致性交通流预测研究  

Research on Spatio-temporally Consistent Traffic Flow Prediction Based on Adaptive Dynamic Correlation Matrix

作　　者：侯越[1] 周瑞娟 张鑫 HOU Yue;ZHOU Ruijuan;ZHANG Xin(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730070

出　　处：《兰州交通大学学报》2024年第6期42-53,共12页Journal of Lanzhou Jiaotong University

基　　金：国家自然科学基金(62063014,62363020);甘肃省自然科学基金(22JR5RA365)。

摘　　要：针对现有交通流预测研究中时空内在关联性提取不足、路网空间动态性表征不充分的问题,提出一种基于自适应动态空间关联度矩阵的交通流预测模型,该模型通过在Transformer_encoder模型内部嵌入空间特征提取模块、多特征分级融合模块与其多头自注意力机制结合,构建时空特征一致性提取模块,实现交通流时空内在关联性的充分提取。其中,空间特征提取模块通过构建自适应动态空间关联度矩阵,以充分表征当前路网邻域的动态空间特征,并通过构建空间特征筛选网络以消除冗余信息,筛选关键影响特征。此外,多特征分级融合模块通过分级融合交通流原始时序特征和时空特征,以实现细致全面的时空相关性挖掘。实验在高速路网PeMS04、PeMS08公开数据集上进行测试,结果表明,所提模型相较于其他基准线模型具有更优的预测效果。To address the issues of insufficient extraction of spatio-temporal intrinsic correlation and inadequate characterization of spatial dynamics in existing traffic flow prediction research,a traffic flow prediction model is proposed based on an adaptive dynamic spatial correlation matrix.The model constructs a spatio-temporal feature consistency extraction module by embedding a spatial feature extraction module and a multi-feature hierarchical fusion module inside the Transformerencoder model,combined with its multi-head self-attention mechanism,to achieve the full extraction of the spatio-temporal intrinsic correlation of traffic flow.The spatial feature extraction module constructs the adaptive dynamic spatial correlation matrix to fully characterize the dynamic spatial features of the road network's neighborhood,while the spatial feature filtering network eliminates redundant information and highlights key influential features.Additionally,the multi-feature hierarchical fusion module conducts detailed and comprehensive mining of spatial and temporal correlations by hierarchically fusing the original temporal and spatial features of traffic flow.The experiments are tested on the PeMS04 and PeMS08 public datasets of the high-speed road network,and the results show that the proposed model has better prediction results compared to other baseline models.

关 键 词：交通流预测 时空相关性 动态性 TRANSFORMER 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]