标题:Travel Time Forecasting with Combination of Spatial-Temporal and Time Shifting Correlation in CNN-LSTM Neural Network
作者:Wei, Wenjing; Jia, Xiaoyi; Liu, Yang; Yu, Xiaohui
通讯作者:Yu, Xiaohui;Yu, XH;Yu, XH
作者机构:[Wei, Wenjing; Jia, Xiaoyi; Liu, Yang; Yu, Xiaohui] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China.; [Yu, Xiaohui] Y 更多
会议名称:2nd International Joint Conference on Asia-Pacific Web (APWeb) / Web-Age Information Management (WAIM)
会议日期:JUL 23-25, 2018
来源:WEB AND BIG DATA (APWEB-WAIM 2018), PT I
出版年:2018
卷:10987
页码:297-311
DOI:10.1007/978-3-319-96890-2_25
关键词:Travel time prediction; Time shifting feature; CNN-LSTM neural network
摘要:The problem of short-term travel time estimation has been intensively investigated recently. However, accurate travel time predicting is still a challenge due to dynamic changes of the traffic and the difficulty of extracting urban traffic data features. In this paper, we mainly focus on time shifting feature of urban roads, which represents the impact of the upstream sections that will be conveyed to the down- stream sections after a certain period of time Delta t. Firstly, we obtain the spatial relationships of the traffic time with Kullback-Leibler divergence (KL-divergence) and urban road networks. Then a Convolutional Neural Network (CNN) module is adopted to extract the spatial-temporal and time shifting information of the target road. Finally, a novel deep architecture combined CNN and Long-short Term Memory Recurrent Neural Network (LSTM) is utilized to predict the short-term travel time. The experimental result on the real data set shows that the proposed model is more effective than other existing approaches.
收录类别:CPCI-S;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-s2.0-85050556308&doi=10.1007%2f978-3-319-96890-2_25&partnerID=40&md5=c70e485118b2845bbdc2c38f6a3b89ae
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