Short‐term traffic congestion prediction with Conv–BiLSTM considering spatio‐temporal features-Reference-Cited by-同舟云学术

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Short‐term traffic congestion prediction with Conv–BiLSTM considering spatio‐temporal features

Published:2020-12 Issue:14 Volume:14 Page:1978-1986
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Li Tao¹,Ni Anning¹,Zhang Chunqin²,Xiao Guangnian³,Gao Linjie¹

Affiliation:

1. School of Naval Architecture, Ocean & Civil EngineeringShanghai Jiao Tong UniversityShanghai200240People's Republic of China

2. School of Civil Engineering and ArchitectureZhejiang Sci‐Tech UniversityHangzhou310018People's Republic of China

3. School of Economics and ManagementShanghai Maritime University1550 Haigang Ave201306ShanghaiPeople's Republic of China

Funder

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.2020.0406

Reference50 articles.

1. Short‐term traffic forecasting: where we are and where we're going;Vlahogianni E.I.;Transp. Res. C, Emerg. Technol.,2014

2. Traffic prediction using multivariate nonparametric regression;Clark S.;J. Transp. Eng.,2003

3. Short‐term traffic flow rate forecasting based on identifying similar traffic patterns;Habtemichael F.G.;Transp. Res. C, Emerg. Technol.,2016

4. Lv L. Chen M. Liu Y. et al.: ‘A plane moving average algorithm for short‐term traffic flow prediction’.Pacific–Asia Conf. on Knowledge Discovery and Data Mining Ho Chi Minh City Vietnam 2015 pp.357–369

5. Experienced travel time prediction for congested freeways;Yildirimoglu M.;Transp. Res. B, Methodol.,2013

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