Analysis of Traffic Data for Congestion Detection Using Machine Learning Algorithms
Subject Areas : AI and RoboticsNahid Amani 1 , Mohammad-Hossein Amerimehr 2 , Sara Efazati 3 , Ali Javidani 4
1 - ICT Research Institute, Tehran, Iran
2 - ICT Research Institute, Tehran, Iran
3 -
4 - School of Electrical and Computer Engineering, College of Engineering, University of Tehran
Keywords: Congestion Detection, Machine Learning, XGBoost, Evaluation Criteria of ML Algorithms.,
Abstract :
Congestion detection is one of the basic elements in guaranteeing the quality of service and is one of the most important measures of efficiency in new-generation telecommunication networks. Congestion leads to the loss of information packets in the network because the sending rate is higher than the capacity in some network links. To control congestion in the network, the first step is to distinguish between the loss of packets due to congestion with the packet loss introduced by other cases, including link failure. Because if the source of the loss is mistakenly identified as congestion, reducing the sending rate in the transmitter does not mitigate the congestion and only reduces throughput and quality of service. Therefore, the main problem of this paper is to identify congestion and distinguish it from the error caused by communication links in a traffic data sample. In solving this problem, various supervised machine learning algorithms including decision tree, random forest, support vector machine, logistic regression, K-nearest neighbor, XGBoost, and neural network have been used. The mentioned algorithms have been evaluated based on different criteria and compared with each other.
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