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：

期：

页码：

78-88

DOI：

10.1016/j.future.2018.06.021

基金类别：

National Natural Science Foundation of China#&#&#61370097#&#&#61602164#&#&#61672217#&#&#61702172#&#&#2018JJ2063#&#&#2018JJ3076#&#&#2016YFB0200405 Natural Science Foundation ofHunan Province#&#&#61370097#&#&#61602164#&#&#61672217#&#&#61702172#&#&#2018JJ2063#&#&#2018JJ3076#&#&#2016YFB0200405 National Key Research and Development Program of China#&#&#61370097#&#&#61602164#&#&#61672217#&#&#61702172#&#&#2018JJ2063#&#&#2018JJ3076#&#&#2016YFB0200405

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 the fuzzy theory and the deep residual network mode...

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

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