KF-TGCN: An Approach to Integrate Expert Knowledge with Graph Convolutional Network for Traffic Prediction

There has been continuous research efforts on improving traffic prediction accuracy by exploring the complicated and dynamic spatio-temporal correlation among road network nodes, and it is a challenging problem especially for midto long-term traffic prediction when limited historical data is available. This study proposes a novel knowledge fusion temporal graph convolutional network (KF-TGCN) model to integrate expert knowledge with inherent spatio-temporal correlation which is captured by temporalgraph convolutional network (T-GCN). Our KF-TGCN model has been employed to predict the traffic flow of California highway. Experiment results show that the KF-TGCN model is capable to provide more promising prediction results and significantly reduce the computational complexity comparing to existing models such as gated recurrent units (GRU) and T-GCN.