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A novel urban traffic prediction mechanism for smart city using learning approach

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成果类型:
期刊论文、会议论文
作者:
Niu, Xiaoguang*;Zhu, Ying;Cao, Qingqing;Zhao, Lei;Xie, Wei(谢伟
通讯作者:
Niu, Xiaoguang
作者机构:
[Zhu, Ying; Cao, Qingqing; Niu, Xiaoguang; Zhao, Lei] Wuhan Univ, Comp Sch, Wuhan 430072, Peoples R China.
[Xie, Wei] Cent China Normal Univ, Comp Sch, Wuhan, Peoples R China.
通讯机构:
[Niu, Xiaoguang] W
Wuhan Univ, Comp Sch, Wuhan 430072, Peoples R China.
语种:
英文
关键词:
Environmental resources;Intelligent transportation system;Smart city;Temporal feature learning;Traffic flow condition prediction
期刊:
Communications in Computer and Information Science
ISSN:
1865-0929
年:
2015
卷:
501
页码:
548-557
会议名称:
8th China Wireless Sensor Network Conference (CWSN)
会议论文集名称:
Communications in Computer and Information Science
会议时间:
OCT 31-NOV 02, 2014
会议地点:
NW Univ, Xian, PEOPLES R CHINA
会议主办单位:
NW Univ
会议赞助商:
China Comp Federat, China Comp Federat Tech Comm on Sensor Network, Springer
主编:
Sun, L Ma, H Fang, D Niu, J Wang, W
出版地:
HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
出版者:
SPRINGER-VERLAG BERLIN
ISBN:
978-3-662-46981-1; 978-3-662-46980-4
基金类别:
This work was partially supported by National Key Basic Research Program of China “973 Project” (Grant No. 2011CB707106), Development Program of China “863 Project” (Grant No. 2013AA122301), National Natural Science Foundation of China “NSFC” (Grant No. 61103220, 61303212) and the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT1278).
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
Traffic flow condition prediction is a basic problem in the transportation field. It is challenging to play out full potential of temporally-related information and overcome the problem of data sparsity existed in the traffic flow prediction. In this paper, we propose a novel urban traffic prediction mechanism namely C-Sense consisting of two parts: CRF-based temporal feature learning and sequence segments matching. CRF-based temporal feature learning exploits a linear-chain condition random field (CRF) to explore the temporal transformation rule in the traffic flow state sequence with supplem...

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