Prediction Model for Traffic Flow with Missing Values Based on Generative Adversarial and Graph Convolutional Networks

In order to improve the accuracy of urban road network traffic flow prediction with missing values, the generator and discriminator of the generative adversarial network are reconstructed, the loss function is improved, and the traffic generative adversarial imputation network (TGAIN) is proposed for the completion of the missing data of traffic flow. Based on empirical mode decomposition (EMD), graph convolutional networks (GCN) and gated recurrent unit (GRU), EMD-GCN-GRU model is designed for urban road network traffic flow prediction. First, the traffic flow data is processed by empirical mode decomposition and each component of the same level is reconstructed as the input of the subsequent prediction model. Then, the graph convolutional networks are used to learn the road network topology to capture the spatial characteristic of traffic flow, and the gated recurrent unit is employed to capture the temporal characteristic of traffic flow. For the road network traffic flow data with missing values, TGAIN is used to complete the data, and then EMD-GCN-GRU is used to predict the traffic flow. The Shenzhen average vehicle speed data set is used to construct a variety of typical traffic flow data with different missing patterns and different missing rates to simulate the actual missing situation. The effectiveness of the method is verified on the ModelArts development platform. The results show that compared with the commonly used matrix factorization imputation method, the TGAIN model has higher completion accuracy in the random missing mode of the dataset and has better completion performance when the non-random missing rate is lower than 50%. Compared with other seven prediction algorithms, the proposed prediction method has higher prediction accuracy. Combining the data imputation method TGAIN with the traffic flow prediction method EMD-GCN-GRU for urban road network traffic flow prediction with missing values can significantly reduce the negative impact of missing data and data noise on traffic flow prediction and capture the spatial and temporal correlation of network traffic flow, which improves the accuracy of urban road network traffic flow prediction.