Traffic Prediction by Augmenting Cellular Data with Non-Cellular Attributes

Prediction of user traffic in cellular networks is one of the promising ways to improve resource utilization among base stations. In this study, we employ deep learning techniques, specifically a long-short-term memory module to forecast cellular traffic. We consider traffic from neighboring cells and other non-cellular traffic-related attributes such as weather, busy period data from open-source API as features to augment the cellular traffic data and improve prediction. Specifically, we augment cellular traffic data from the City of Milan and its surroundings and we perform two types of analyses: (i) a one-step prediction or a point-by-point forecast of traffic and (ii) a trend analysis which is the forecast of traffic over an extended period. We compare the results with existing statistical methods such as auto-regression integrated moving averages (ARIMA) and exponential smoothing and observe gains in the trend analysis by providing the augmented data, whereas the one-step prediction is not much impacted.