The rapid growth of mobile traffic and the emergence of advanced mobile services and infrastructures are shifting significant attention toward the cellular network back-hauling infrastructure. At this network segment, there is a growing interest in understanding spatio-temporal mobile traffic distributions at different network levels, in order to better define flexible networking solutions for forthcoming smart 5G infrastructures including, for instance, mobile edge computing features. In this work we study these aspects and characterize the load on cellular access networks using real-world anonymized subscriber data, from the Lyon metropolitan area in France, providing statistical distribution to the research community. We find that the traffic distribution at Node-B level is best fit by a Weibull distribution, and that at the radio network aggregation it is best fit by a hybrid Weibull-Pareto distribution.