<p>We present an integrated graph-based neural network architecture for predicting campus building occupancy and inter-building movement at dynamic temporal resolution that learns traffic flow patterns from Wi-Fi logs combined with the usage schedules within the buildings. The relative traffic flows are directly estimated from the WiFi data without assuming the occupant behaviour or preferences while maintaining individual privacy. We formulate the problem as a data-driven graph structure represented by a set of nodes (representing buildings), connected through a route of edges or links using a novel Graph Convolution plus LSTM Neural Network (GCLSTM), which has shown remarkable success in modelling complex patterns. We describe the formulation, model estimation, and interpretability, as well as examine the relative performance of our proposed model. We also present an illustrative architecture of the models and apply them to real-world WiFi logs collected at the Toronto Metropolitan University campus. The results of the experiments show that the integrated GCLSTM models significantly outperform traditional pedestrian flow estimators like the Multi Layer Perceptron (MLP) and Linear Regression.</p>