Research on Regional Traffic Flow Prediction Based on MGCN-WOALSTM-Reference-Cited by-同舟云学术

Research on Regional Traffic Flow Prediction Based on MGCN-WOALSTM

Published:2023 Issue: Volume:11 Page:126436-126446
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Cao Kerang¹,Liu Yaru¹^ORCID,Duan Lini²,Xu Sen¹^ORCID,Jung Hoekyung³^ORCID

Affiliation:

1. Key Laboratory of Intelligent Technology of Chemical Process Industry in Liaoning Province, College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, China

2. Department of Big Data Management and Application, Shenyang University of Chemical Technology, Shenyang, China

3. Department of Computer Engineering, Pai Chai University, gwangyeoksi, South Korea

Funder

Ministry of Science and ICT, South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10310207.pdf?arnumber=10310207

Reference27 articles.

1. A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction

2. A combined traffic flow forecasting model based on graph convolutional network and attention mechanism

3. Summary of research on impact of adverse weather on traffic safety and traffic flow;yang;J Wuhan Univ Technol Transp Sci Eng,2019

4. A Novel Method for Analyzing Weather Effect on Smart City Traffic

5. IGAGCN: Information geometry and attention-based spatiotemporal graph convolutional networks for traffic flow prediction

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