Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles-Reference-Cited by-同舟云学术

Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

Published:2023 Issue: Volume:11 Page:58516-58531
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hussain Abdul Hussain Ali¹,Taher Montadar Abas²,Mahmood Omar Abdulkareem²^ORCID,Hammadi Yousif I.³^ORCID,Alkanhel Reem⁴^ORCID,Muthanna Ammar⁵^ORCID,Koucheryavy Andrey⁶

Affiliation:

1. Department of Architectural Engineering, College of Engineering, University of Diyala, Baqubah, Diyala, Iraq

2. Department of Communications Engineering, College of Engineering, University of Diyala, Baqubah, Diyala, Iraq

3. Department of Medical Instruments Engineering Techniques, Bilad Alrafidain University College, Baqubah, Diyala, Iraq

4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

5. Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia

6. Department of Telecommunication Networks and Data Transmission, Bonch-Bruevich Saint Petersburg State University of Telecommunications, Saint Petersburg, Russia

Funder

Princess Nourah bint Abdulrahman University Researchers Supporting Project

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; and the studies at St. Petersburg State University of Telecommunications. prof. M.A. Bonch-Bruevich

Ministry of Science and High Education of the Russian Federation

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/ielx7/6287639/10005208/10108955.pdf?arnumber=10108955

Reference36 articles.

1. Short-term traffic flow prediction using a methodology based on ARIMA and RBF-ANN

2. Empirical evaluation of gated recurrent neural networks on sequence modeling;chung;arXiv 1412 3555,2014

3. Deep neural networks for traffic flow prediction;yi;Proc IEEE Int Conf Big Data Smart Comput (BigComp),2017

4. Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data

5. Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion

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