Data Matters: The Case of Predicting Mobile Cellular Traffic

TL;DR

This paper demonstrates that incorporating road traffic data such as flow and speed significantly improves the accuracy of cellular load predictions, reducing errors by over 50%, which benefits network resource management in smart city contexts.

Contribution

It introduces a novel approach of using population dynamics data, specifically road traffic measures, to enhance cellular traffic load prediction accuracy.

Findings

Road traffic data reduces prediction errors by up to 56.5%.

Combining cellular metrics with road flow and speed improves accuracy.

The approach benefits smart city infrastructure and network management.

Abstract

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.