用道路车流数据提升基站流量预测准确率，最高降误差56.5%
Data Matters: The Case of Predicting Mobile Cellular Traffic
- 融合道路车流与速度数据，建模基站负载生成机制
- 引入交通数据后，预测误差最高降低56.5%
- 适合智慧交通与网络优化研究者参考
精准预测基站流量负载对移动运营商及用户至关重要，有助于高效利用网络资源，并支撑智慧城市与智慧道路服务。传统方法仅依赖蜂窝网络时序数据，近期也尝试引入兴趣点等外部因素。本文提出通过人口动态数据建模基站负载生成过程，聚焦智慧道路场景，利用道路流量与速度数据提升预测精度。大量实验表明，结合道路流速与流量及蜂窝网络指标，可显著降低基站负载预测误差，最高达56.5%。代码、可视化与完整结果见https://github.com/nvassileva/DataMatters。
原文摘要 · Abstract (English)
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.
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