SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting-Reference-Cited by-同舟云学术

SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting

Published:2019-05-22 Issue:9 Volume:13 Page:1348-1355
ISSN:1751-9578
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET Intelligent Transport Systems

Author:

Cai Lingru¹,Chen Qian¹,Cai Weihong¹,Xu Xuemiao²,Zhou Teng¹³^ORCID,Qin Jing³

Affiliation:

1. Department of Computer ScienceCollege of Engineering, Shantou UniversityShantouPeople's Republic of China

2. Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace InformationSchool of Computer Science and Technology, South China University of TechnologyGuangzhouPeople's Republic of China

3. Center for Smart Health, School of Nursing, The Hong Kong Polytechnic UniversityHong Kong

Funder

Natural Science Foundation of Guangdong Province

Hong Kong Polytechnic University

Science and Technology Planning Project of Guangdong Province

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2018.5315

Reference51 articles.

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4. Time series analysis: forecasting and control;Box G.E.P.;J. Time,1976

5. Forecasting model of traffic flow based on ARMA and wavelet transform;Huang D.;Comput. Eng. Appl.,2006

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