Traditional traffic prediction, limited by the scope of sensor data, falls short in comprehensive traffic management. Mobile networks offer a promising alternative using network activity counts, but these lack crucial directionality. Thus, we present the TeltoMob dataset, featuring undirected telecom counts and corresponding directional flows, to predict directional mobility flows on roadways. To address this, we propose a two-stage spatio-temporal graph neural network (STGNN) framework. The first stage uses a pre-trained STGNN to process telecom data, while the second stage integrates directional and geographic insights for accurate prediction. Our experiments demonstrate the framework's compatibility with various STGNN models and confirm its effectiveness. We also show how to incorporate the framework into real-world transportation systems, enhancing sustainable urban mobility.

翻译：传统交通预测受限于传感器数据范围，难以实现全面的交通管理。移动网络通过使用网络活动计数提供了一种有前景的替代方案，但这些数据缺乏关键的方向性信息。为此，我们提出了TeltoMob数据集，该数据集包含无向电信计数及相应的定向流量，用于预测道路上的定向移动流量。为解决此问题，我们提出了一种两阶段时空图神经网络（STGNN）框架。第一阶段使用预训练的STGNN处理电信数据，第二阶段则整合方向性与地理信息以实现精准预测。实验表明该框架能与多种STGNN模型兼容，并验证了其有效性。我们还展示了如何将该框架整合到实际交通系统中，以提升可持续城市交通管理水平。