The main contribution reported in the paper is a novel paradigm through which mobile cellular traffic forecasting is made substantially more accurate. Specifically, by incorporating freely available road metrics we characterise the data generation process and spatial dependencies. Therefore, this provides a means for improving the forecasting estimates. We employ highway flow and average speed variables together with a cellular network traffic metric in a light learning structure to predict the short-term future load on a cell covering a segment of a highway. This is in sharp contrast to prior art that mainly studies urban scenarios (with pedestrian and limited vehicular speeds) and develops machine learning approaches that use exclusively network metrics and meta information to make mid-term and long-term predictions. The learning structure can be used at a cell or edge level, and can find application in both federated and centralised learning.

翻译：本文的主要贡献在于提出了一种新型范式，显著提升了移动蜂窝流量预测的准确性。具体而言，通过整合免费获取的道路指标，我们刻画了数据生成过程及空间依赖性，从而为改进预测估计提供了途径。我们采用高速公路流量与平均速度变量，结合蜂窝网络流量指标，构建轻量级学习结构，用于预测覆盖高速公路路段的蜂窝基站的短期未来负载。这与现有研究形成鲜明对比——现有工作主要聚焦城市场景（涉及行人及有限车辆速度），并开发仅依赖网络指标与元信息进行中长期预测的机器学习方法。该学习结构可部署于蜂窝基站或网络边缘层级，并适用于联邦学习与集中式学习两种场景。