  Modern intelligent transportation systems provide data that allow real-time
dynamic demand prediction, which is essential for planning and operations. The
main challenge of prediction of dynamic Origin-Destination (O-D) demand
matrices is that demands cannot be directly measured by traffic sensors;
instead, they have to be inferred from aggregate traffic flow data on traffic
links. Specifically, spatial correlation, congestion and time dependent factors
need to be considered in general transportation networks. In this paper we
propose a novel O-D prediction framework combining heterogeneous prediction in
graph neural networks and Kalman filter to recognize spatial and temporal
patterns simultaneously. The underlying road network topology is converted into
a corresponding line graph in the newly designed Fusion Line Graph
Convolutional Networks (FL-GCNs), which provide a general framework of
predicting spatial-temporal O-D flows from link information. Data from New
Jersey Turnpike network are used to evaluate the proposed model. The results
show that our proposed approach yields the best performance under various
prediction scenarios. In addition, the advantage of combining deep neural
networks and Kalman filter is demonstrated.
