  Vehicle flow, a crucial indicator for transportation, is often limited by
detector coverage. With the advent of extensive mobile network coverage, we can
leverage mobile user activities, or cellular traffic, on roadways as a proxy
for vehicle flow. However, as counts of cellular traffic may not directly align
with vehicle flow due to data from various user types, we present a new task:
predicting vehicle flow in camera-free areas using cellular traffic. To uncover
correlations within multi-source data, we deployed cameras on selected roadways
to establish the Tel2Veh dataset, consisting of extensive cellular traffic and
sparse vehicle flows. Addressing this challenge, we propose a framework that
independently extracts features and integrates them with a graph neural network
(GNN)-based fusion to discern disparities, thereby enabling the prediction of
unseen vehicle flows using cellular traffic. This work advances the use of
telecom data in transportation and pioneers the fusion of telecom and
vision-based data, offering solutions for traffic management.
