标题:Short-term Traffic Flow Forecasting Using Transfer Ratio and Road Similarity
作者:Guo, De ;Chen, Meng ;Yu, Xiaohui ;Liu, Yang
通讯作者:Yu, Xiaohui
作者机构:[Guo, De ;Yu, Xiaohui ;Liu, Yang ] School of Computer Science and Technology, Shandong University, Jinan, Shandong; 250101, China;[Liu, Yang ] Departm 更多
会议名称:2018 International Joint Conference on Neural Networks, IJCNN 2018
会议日期:8 July 2018 through 13 July 2018
来源:Proceedings of the International Joint Conference on Neural Networks
出版年:2018
卷:2018-July
DOI:10.1109/IJCNN.2018.8489132
关键词:BP Neural Network; CBOW; Time Series; Traffic Flow Forecasting; Transfer Ratio
摘要:It is essential to make accurate traffic flow forecast or the Intelligent Transportation Systems (ITS). To improve the forecasting accuracy, most existing methods mainly focus on the time-series data. In this paper, we propose a novel model combining transfer ratio and road similarity (CoTRRS) to forecast the short-term traffic flow, which makes full use of the spatial information in the urban road networks. We first utilize the Continuous Bag-of-Words (CBOW) model to extract the road similarity and then combine it with transfer ratio to make accurate forecast using a back propagation neural network. To verify our model, we have performed extensive experiments on a real dataset, and the empirical study reveals that CoTRRS outperforms baselines. © 2018 IEEE.
收录类别:EI;SCOPUS
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-s2.0-85056531246&doi=10.1109%2fIJCNN.2018.8489132&partnerID=40&md5=389ef0a8980cd4b84970a71e7cfb6694
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