  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.
