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Prediction of traffic in a public safety network

Traditional statistical analysis and mining of network data are often employed to determine traffic distribution, to summarize a user's behavior patterns, or to predict future network traffic. We analyze three months of network log data from a deployed public safety trunked radio network. After data cleaning and traffic extraction, we apply the K-means algorithm and identify that three clusters of talk groups best reflect users' behavior patterns represented by the hourly number of calls. We propose a traffic prediction model by applying the classical SARIMA models on clusters of users. The predicted network traffic agrees with the collected traffic data and the proposed cluster-based prediction approach performs well compared to the prediction based on the aggregate traffic