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An Online-Traffic-Prediction Based Route Finding Mechanism for Smart City

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成果类型:
期刊论文
作者:
Niu, Xiaoguang*;Zhu, Ying;Cao, Qingqing;Zhang, Xining;Xie, Wei(谢伟);...
通讯作者:
Niu, Xiaoguang
作者机构:
[Zhu, Ying; Cao, Qingqing; Niu, Xiaoguang; Zhang, Xining] Wuhan Univ, Sch Comp Sci, Wuhan 430000, Peoples R China.
[Xie, Wei] Cent China Normal Univ, Comp Sch, Wuhan 430000, Peoples R China.
[Zheng, Kun] China Univ Geosci, Fac Informat Engn, Wuhan 430000, Peoples R China.
通讯机构:
[Niu, Xiaoguang] W
Wuhan Univ, Sch Comp Sci, Wuhan 430000, Peoples R China.
语种:
英文
关键词:
Dynamics;Forecasting;Intelligent systems;Linear transformations;Mathematical transformations;Random processes;Roads and streets;Social networking (online);Taxicabs;Traffic control;Transportation;Travel time;Conditional random field;Dynamic patterns;Environmental information;Environmental resources;Intelligent transportation systems;Temporal transformations;Traffic conditions;Travel time prediction;Transportation routes
期刊:
International Journal of Distributed Sensor Networks
ISSN:
1550-1477
年:
2015
卷:
11
期:
8
页码:
970256:1-970256:16
基金类别:
National Key Basic Research Program of China "973 Project"National Basic Research Program of China [2011CB707106]; Development Program of China "863 Project"National High Technology Research and Development Program of China [2013AA-122301]; National Natural Science Foundation of China "NSFC"National Natural Science Foundation of China (NSFC) [41127901-06, 61303212, 61373169]; Program for Changjiang Scholars and Innovative Research Team in UniversityProgram for Changjiang Scholars & Innovative Research Team in University (PCSIRT) [IRT1278]; Natural Science Foundation of Hubei Province of ChinaNatural Science Foundation of Hubei Province [2014CFB191]
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
Finding fastest driving routes is significant for the intelligent transportation system. While predicting the online traffic conditions of road segments entails a variety of challenges, it contributes much to travel time prediction accuracy. In this paper, we propose O-Sense, an innovative online-traffic-prediction based route finding mechanism, which organically utilizes large scale taxi GPS traces and environmental information. O-Sense firstly exploits a deep learning approach to process spatial and temporal taxi GPS traces shown in dynamic patterns. Meanwhile, we model the traffic flow stat...

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