MTP-NT: A Mobile Traffic Predictor Enhanced by Neighboring and Transportation Data

The development of techniques able to forecast the mobile network traffic in a city can feed data driven applications, as Virtual Network Functions (VNF) orchestrators, optimizing the resource allocation and increasing the capacity of mobile networks. Despite the fact that several studies have addressed this problem, many did not consider neither the traffic relationship among city regions nor the information retrieved from public transport stations, which may provide useful information to better anticipate the network traffic. In this paper, we propose a new deep learning based architecture to forecast the network traffic using representation learning and recurrent neural networks. The framework, named Mobile Traffic Predictor Enhanced by Neighboring and Transportation Data (MTP-NT), has two major components: the first one is responsible of learning from the time series of the region to be predicted, with the second one learning from the time series of both neighboring regions and public transportation stations. Several experiments were conducted over a dataset from the city of Milan, as well as comparisons against widely adopted and state-of-the-art techniques. The results shown in this paper demonstrate that the usage of public transport information contributes to improve the forecasts in central areas of the city, as well as in regions with aperiodic demands, such as tourist regions.