标题:A Data Grouping CNN Algorithm for Short-Term Traffic Flow Forecasting
作者:Yu, Donghai; Liu, Yang; Yu, Xiaohui
通讯作者:Liu, Yang
作者机构:[Yu, Donghai; Liu, Yang; Yu, Xiaohui] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China.; [Yu, Xiaohui] York Univ, Sch Informat T 更多
会议名称:18th Asia-Pacific Web Conference (APWeb)
会议日期:SEP 23-25, 2016
来源:Web Technologies and Applications, Pt I
出版年:2016
卷:9931
页码:92-103
DOI:10.1007/978-3-319-45814-4_8
关键词:Convolution Neural Network; Traffic flow forecasting; CBOW; Deep; learning
摘要:In this paper, a data grouping approach based on convolutional neural network (DGCNN) is proposed for forecasting urban short-term traffic flow. This approach includes the consideration of spatial relations between traffic locations, and utilizes such information to train a convolutional neural network for forecasting. There are three advantages of our approach: (1) the spatial relations of traffic flow are adopted; (2) high-quality features are extracted by CNN; and (3) the accuracy of forecasting short-term traffic flow is improved. To verify our model, extensive experiments are performed on a real data set, and the result shows that the model is more effective than other existing methods.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:2
Scopus被引频次:4
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989332237&doi=10.1007%2f978-3-319-45814-4_8&partnerID=40&md5=5cc6694d98a15a7c269655e89393e559
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