基于时空多视野注意残差网络的城市区域交通流量预测  

City Regional Traffic Flow Prediction Based on Spatiotemporal Multi-view Attention Residual Network

作　　者：陈静[1] 杨国威 张昭冲 王伟 Chen Jing;Yang Guowei;Zhang Zhaochong;Wang Wei(School of Information Technology and Engineering,Tianjin University of Technology and Education,Tianjin 300222,China)

机构地区：[1]天津职业技术师范大学信息技术工程学院,天津300222

出　　处：《系统仿真学报》2025年第3期607-622,共16页Journal of System Simulation

基　　金：天津市教委科研计划(2021KJ008);天津市津南区科技计划(20220105)。

摘　　要：为高效、全面提取城市中复杂的时空相关性,提出一种新的端到端的深度学习框架—时空多视野注意残差网络(spatiotemporal multi-view attention residual network, ST-MVAR),用于城市区域交通流量预测。整合交通流量的临近性、周期性、趋势性和外部因素作为网络输入,该网络通过跳跃连接,形成多层嵌套残差网络结构;设计多视野扩展模块,用于捕获交通流量对不同距离的空间依赖;引入坐标注意力机制,有效建立交通流量的时空相关性;通过K-Means聚类方法获取各时段交通流量所属模式,作为额外特征,进一步提高模型的预测精度。实验结果表明:ST-MVAR使用更少的参数获得更高的性能,相比之前的方法 RMSE降低14.2%。However,efficiently and comprehensively capturing the complex spatiotemporal correlations within urban traffic flow presents a key challenge.Existing research methods struggle to fully capture these spatiotemporal dependencies.To address these issues,we propose a novel end-to-end deep learning framework called the spatiotemporal multi-view attention residual network(ST-MVAR)for predicting traffic flow in urban areas.we integrate the proximity,periodicity,trend,and external factors of traffic flow as inputs to the network.This network employs skip connections to form a multi-layer nested residual network structure.Additionally,we design a Multi-View Extension module to capture spatial dependencies of traffic flow at various distances and introduce a coordinate attention network to effectively establish the spatiotemporal correlations within traffic flow.Furthermore,we use the k-means clustering method to obtain patterns for each cross-sectional time traffic flow and incorporate them as additional features to further enhance the model's predictive accuracy Experimental results demonstrate that ST-MVAR achieves higher performance with fewer parameters,14.2%lower RMSE compared to the best previous methods.

关 键 词：交通流量预测 残差网络 视野扩展 坐标注意力 K-MEANS聚类 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]