Measurement-driven mobile data traffic modeling in a large metropolitan area

Understanding mobile data traffic demands is crucial to the evaluation of strategies addressing the problem of high bandwidth usage and scalability of network resources, brought by the pervasive era. In this paper, we conduct the first detailed measurement-driven modeling of smartphone subscribers' mobile traffic usage in a metropolitan scenario. We use a large-scale dataset collected inside the core of a major 3G network of Mexico's capital. We first analyse individual subscribers routine behavior and observe identical usage patterns on different days. This motivates us to choose one day for studying the subscribers' usage pattern (i.e., “when” and “how much” traffic is generated) in detail. We then classify the subscribers in four distinct profiles according to their usage pattern. We finally model the usage pattern of these four subscriber profiles according to two different journey periods: peak and non-peak hours. We show that the synthetic trace generated by our data traffic model consistently imitates different subscriber profiles in two journey periods, when compared to the original dataset.