WT-2DCNN: A convolutional neural network traffic flow prediction model based on wavelet reconstruction

Abstract

Accurate traffic flow prediction is important for congestion identification and traffic dispersion. The original traffic flow data may generate different noises in the detector collection process and data aggregation process, resulting in large errors in the prediction results.To solve the problems above, this paper proposes a convolutional neural network model based on wavelet reconstruction (WT-2DCNN), which eliminates potential outliers in the data by introducing wavelet method, and constructs a WT-2DCNN model based on data extension and fusion of convolutional neural networks, and obtains the internal trend features of traffic flow through multiple convolutional and pooling layers in this model for traffic flow prediction.In this paper, the performance and training efficiency of the WT-2DCNN model has been validated on the publicly available Caltrans Performance Measurement System (PeMS) dataset, and the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are significantly lower than those of other algorithms, and the training time is only 1/4 to 1/3 of that of Recurrent Neural Networks (RNN), indicating that the WT-2DCNN model has higher accuracy and more efficient training efficiency.