Data Matters: The Case of Predicting Mobile Cellular Traffic

2024-10-21

IEEE Vehicular Networking Conference

AI Summary

To address the insufficient accuracy of base station traffic load forecasting—which hinders smart city and intelligent road services—this paper proposes a multi-source time-series forecasting method incorporating prior knowledge from traffic flow dynamics. For the first time, crowd mobility data—including vehicle count and speed—are leveraged as core external priors, replacing conventional approaches that rely solely on endogenous network time series or static points-of-interest (POIs), thereby revealing the underlying human mobility mechanisms driving traffic load generation. Our framework integrates multi-source temporal alignment, sliding-window modeling, adaptive normalization, and error-weighted regression. Evaluated on real-world urban datasets, it achieves up to 56.5% reduction in prediction error, significantly outperforming both pure time-series models and POI-augmented baselines. The source code and visualization results are publicly available.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Predicting mobile cellular traffic load accurately
• Incorporating road traffic data to improve predictions
• Reducing base station load prediction errors significantly

Innovation

• Uses population dynamics data for predictions
• Incorporates road traffic measures for accuracy
• Combines cellular and traffic metrics effectively