User clustering and traffic prediction in a trunked radio system

Traditional statistical analysis of network data is often employed to determine traffic distribution, to summarize user's behavior patterns, or to predict future network traffic. Mining of network data may be used to discover hidden user groups, to detect payment fraud, or to identify network abnormalities. In our research we combine traditional traffic analysis with data mining technique. We analyze three months of continuous network log data from a deployed public safety trunked radio network. After data cleaning and traffic extraction, we identify clusters of talk groups by applying Autoclass tool and K-means algorithm on user's behavior patterns represented by the hourly number of calls. We propose a traffic prediction model by applying the classical SARIMA models on the 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.