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A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features

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成果类型:
期刊论文
作者:
Chen, Weihong;An, Jiyao*;Li, Renfa;Fu, Li;Xie, Guoqi;...
通讯作者:
An, Jiyao
作者机构:
[Li, Keqin; Xie, Guoqi; An, Jiyao; Fu, Li; Li, Renfa; Chen, Weihong] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China.
[Chen, Weihong] Hunan City Univ, Coll Informat & Elect Engn, Changsha, Hunan, Peoples R China.
[Bhuiyan, Md Zakirul Alam] Fordham Univ, Dept Comp & Informat Sci, Bronx, NY 10458 USA.
[Li, Keqin] SUNY Coll New Paltz, Dept Comp Sci, New Paltz, NY 12651 USA.
通讯机构:
[An, Jiyao] H
Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China.
语种:
英文
关键词:
Deep learning;Fuzzy representation;Residual networks;Traffic flow prediction
期刊:
Future Generation Computer Systems
ISSN:
0167-739X
年:
2018
卷:
89
期:
1
页码:
78-88
基金类别:
This study was partially supported by the National Key Research and Development Plan of China under Grant No. 2016YFB0200405 , the National Natural Science Foundation of China with Grant Nos. 61672217 , 61370097 , 61602164 , and 61702172 , and the Natural Science Foundation of Hunan Province, China under Grant 2018JJ2063 and 2018JJ3076 .
机构署名:
本校为其他机构
院系归属:
信息与电子工程学院
摘要:
Predicting traffic flow is one of the fundamental needs to comfortable travel, but this task is challenging in vehicular cyber–physical systems because of ever-increasing uncertain traffic big data. Although deep-learning (DL) methods with outstanding performance recently have become popular, most existing DL models for traffic flow prediction are fully deterministic and shed no light on data uncertainty. In this study, a novel fuzzy deep-learning approach called FDCN is proposed for predicting citywide traffic flow. This approach is built on ...

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