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Modern intelligent transportation systems provide data that allow real-timedynamic demand prediction, which is essential for planning and operations. Themain challenge of prediction of dynamic Origin-Destination (O-D) demandmatrices is that demands cannot be directly measured by traffic sensors;instead, they have to be inferred from aggregate traffic flow data on trafficlinks. Specifically, spatial correlation, congestion and time dependent factorsneed to be considered in general transportation networks. In this paper wepropose a novel O-D prediction framework combining heterogeneous prediction ingraph neural networks and Kalman filter to recognize spatial and temporalpatterns simultaneously. The underlying road network topology is converted intoa corresponding line graph in the newly designed Fusion Line GraphConvolutional Networks (FL-GCNs), which provide a general framework ofpredicting spatial-temporal O-D flows from link information. Data from NewJersey Turnpike network are used to evaluate the proposed model. The resultsshow that our proposed approach yields the best performance under variousprediction scenarios. In addition, the advantage of combining deep neuralnetworks and Kalman filter is demonstrated.

Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter