TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation
  and Prediction via Multifaceted Graph Modeling
ChungYi Lin Shen-Lung Tung Hung-Ting Su Winston H. Hsu
To address the limitations of traffic prediction from location-bound
detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data
source that leverages the extensive coverage of cellular traffic to capture
mobility patterns. Our extensive analysis validates its potential for
transportation. Focusing on vehicle-related GCT flow prediction, we propose a
graph neural network that integrates multivariate, temporal, and spatial facets
for improved accuracy. Experiments reveal our model's superiority over
baselines, especially in long-term predictions. We also highlight the potential
for GCT flow integration into transportation systems.