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.

本文报道了一种新的方法，该方法通过结合免费提供的道路指标来确立数据生成过程和空间依赖性，从而提高了移动蜂窝流量预测的准确性，这可以应用于边缘或中心化学习，可用于预测短期未来一段高速公路覆盖区域的负载。