Data Matters: The Case of Predicting Mobile Cellular Traffic
This work addresses the need for efficient network resource management for mobile cellular operators and users, particularly in smart city contexts, but it is incremental as it builds on existing methods by adding new data sources.
The paper tackles the problem of predicting mobile cellular traffic load for base stations by incorporating road traffic measures, resulting in a substantial reduction of prediction errors by up to 56.5%.
Accurate predictions of base stations' traffic load are essential to mobile cellular operators and their users as they support the efficient use of network resources and allow delivery of services that sustain smart cities and roads. Traditionally, cellular network time-series have been considered for this prediction task. More recently, exogenous factors such as points of interest and other environmental knowledge have been explored too. In contrast to incorporating external factors, we propose to learn the processes underlying cellular load generation by employing population dynamics data. In this study, we focus on smart roads and use road traffic measures to improve prediction accuracy. Comprehensive experiments demonstrate that by employing road flow and speed, in addition to cellular network metrics, base station load prediction errors can be substantially reduced, by as much as $56.5\%.$ The code, visualizations and extensive results are available on https://github.com/nvassileva/DataMatters.