Traffic Flow Forecasting based on Adaptive Graph Structure for Spatio-Temporal Attention Mechanism Network-Reference-Cited by-同舟云学术

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Traffic Flow Forecasting based on Adaptive Graph Structure for Spatio-Temporal Attention Mechanism Network

Published:2023-11-17 Issue: Volume:21 Page:8353-8358
ISSN:
Container-title:2023 China Automation Congress (CAC)
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Author:

Lei Hang¹,Li Mingxin²,Zhao Yuan¹,Wen Shixi¹,Li Chi¹,Zhao Hui³

Affiliation:

1. College of Information Engineering, Dalian University,Dalian,China

2. College of Computer and Information Engineering, Tianjin Normal University,Tianjin,China

3. Institute of School of Artificial Intelligence, Shenyang University of Technology,Shenyang,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10450150/10449968/10451624.pdf?arnumber=10451624

Reference19 articles.

1. A summary of traffic flow forecasting methods;Liu;Journal of highway and transportation research and development,2004

2. Vector autoregressive models for multivariate time series analysis; macroeco-nomic indicators in ghana;Tetteh-Bator;Mathematical Theory and Modeling,2018

3. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

4. Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks

5. Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network

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