摘要
准确的交通流量预测可协助交通管理部门工作,减少交通压力.但现有方法对交通流时间特征与空间特征学习不充分,将二者割裂讨论,忽视了时间与空间的动态相关性.针对以上问题,本文提出了基于ConvGRU的多时间尺度时空卷积交通流预测方法(MTSTC),设计浅层时空卷积模块对数据中的时空相关性进行初步提取;提出以ConvGRU为核心特征提取器的深层时空卷积模块,对数据的时空特征进行更深层次挖掘;并从3种时间尺度范围的数据中提炼交通流的周期性特征;结合注意力机制设计了时空注意力模块辅助模型训练,提升模型收敛速率.在公开数据集PEMS04和PEMS08上进行实验验证,结果表明采用MAE和RMSE评价指标时,本文方法的准确率相较基线方法在两个数据集上提升了3.23%~5.64%.
Traffic flow prediction is an essential part of intelligent transportation system.Accurate traffic flow prediction can not only assist traffic management to reduce traffic pressure but also avoid congestion and traffic accidents.The current methods do not learn the temporal and spatial features of traffic flow adequately,and learn them in isolation,ignoring the intrinsic relationship between them.To address the above issues,this paper proposed a multi-scale spatio-temporal convolutional traffic flow prediction method based on ConvGRU(MTSTC).A shallow spatio-temporal convolution module is conceived to perform preliminary extraction of spatio-temporal correlations in the data;a deep spatio-temporal convolution module with ConvGRU as the core feature extractor is constructed to capture the spatio-temporal features of the data at a deeper level;the periodicity of traffic flow is refined by distilling data from three time scale ranges(recent,current day,and current week);the spatio-temporal attention module is designed in conjunction with the attention mechanism to assist model training and improve the convergence rate.Experiments are conducted on two public datasets,PEMS04 and PEMS08,and the results demonstrate that the MAE and RMSE of MTSTC are reduced by 3.23%~5.64%on the two data sets respectively,compared to the baseline method.
作者
王玉森
景志勇
卫琳
高宇飞
石磊
王清贤
陶永才
王向杰
WANG Yusen;JING Zhiyong;WEI Lin;GAO Yufei;SHI Lei;WANG Qingxian;TAO Yongcai;WANG Xiangjie(School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China;School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China;College of Software Engineering,Zhengzhou University of Light Industry,Zhengzhou 450001,China;Songshan Laboratory,Zhengzhou 450003,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2024年第10期2355-2361,共7页
Journal of Chinese Computer Systems
基金
国家重点研发专项项目(2018YFB1701401,2020YFB1712401)资助
国家自然科学基金青年科学基金项目(62006210,62001284,62206252)资助
2022年度河南省重大科技专项项目(221100210100,221100211200)资助
2020年度河南省重大公益专项项目(201300210500)资助
南阳市协同创新重大专项项目(22XTCX12001)资助
郑州大学高层次人才科研启动基金项目(32340306)资助
河南省科技攻关项目(222102210026,212102210238)资助.
关键词
交通流量预测
时空卷积模块
注意力机制
ConvGRU
traffic flow prediction
spatio-temporal convolutional module
attention mechanism
ConvGRU