The information contained within Call Details records (CDRs) of mobile networks can\nbe used to study the operational efficacy of cellular networks and behavioural pattern of mobile\nsubscribers. In this study, we extract actionable insights from the CDR data and show that there\nexists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can\nbe leveraged by the mobile operators for effective network planning such as resource management\nand optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly\navailable from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework\nfor mobile traffic classification. Experimental results show that the proposed model based on machine\nlearning technique is able to accurately model and classify the network traffic patterns. Furthermore,\nwe demonstrate the application of such insights for resource optimisation.
Loading....