Road traffic state prediction based on a graph embedding recurrent neural network under the SCATS

Road traffic state prediction is one of the essential and vital issues in intelligence transportation system, but it is difficult to get high accuracy due to the complicated spatiotemporal characteristics of traffic flow data, especially under the Sydney coordinated adaptive traffic system. In this work, we represent the traffic road network as a graph and propose a novel traffic flow prediction framework named the graph embedding recurrent neural network (GERNN). It could tackle the difficulty in the road traffic state prediction. We conduct numerical tests to compare GERNN with other existing methods using a real-world dataset.