Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)

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Modeling Traffic Congestion using Graph Convolutional Networks

Authors
Madhwaraj Kango Gopal1, *, A. Asha2, Arpana Prasad3, Akahaury Nimitt Verma4, P. Aditya Venkat Ganesh5
1Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India
2Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India
3Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India
4Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India
5Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India
*Corresponding author.
Corresponding Author
Madhwaraj Kango Gopal
Available Online 17 October 2023.
DOI
10.2991/978-94-6463-250-7_28How to use a DOI?
Keywords
—congestion; networks; deep learning; graph convolutional networks
Abstract

Congestion in a network can cause data packet losses, delays, and reduced network performance. To prevent congestion, network engineers must be able to accurately predict and manage network traffic. In this research paper, we explore the use of Graph Convolutional Networks (GCN) for predicting congestion in a network. GCN is a type of deep learning algorithm that can analyze complex network structures to predict the behavior of nodes in a network. With GCN, predicting congestion in a network, identification of potential congested areas becomes a reality. Proactive measures to prevent congestion from occurring is also been attempted in this research work. The results of our experiments demonstrate that GCN outperforms other conventional machine learning techniques in predicting network congestion with high accuracy and precision using ReLU6, which was the most suitable activation function for implementing the model. This research also demonstrates the potential of using deep learning algorithms such as GCN to improve network management and optimize network performance.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
Series
Advances in Computer Science Research
Publication Date
17 October 2023
ISBN
10.2991/978-94-6463-250-7_28
ISSN
2352-538X
DOI
10.2991/978-94-6463-250-7_28How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Madhwaraj Kango Gopal
AU  - A. Asha
AU  - Arpana Prasad
AU  - Akahaury Nimitt Verma
AU  - P. Aditya Venkat Ganesh
PY  - 2023
DA  - 2023/10/17
TI  - Modeling Traffic Congestion using Graph Convolutional Networks
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 159
EP  - 164
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_28
DO  - 10.2991/978-94-6463-250-7_28
ID  - Gopal2023
ER  -