Local Road Network Traffic Flow Prediction Based on Improved eRCNN

Abstract

Abstract: Error feedback recurrent convolutional neural network can only handle time series error sequences when it is applied to short-time traffic flow forecasting. The spatial topological characteristics implied in traffic flow error data are ignored. Furthermore, at model initialization time, it uses the general convolutional neural network initialization method to reduce the training efficiency of the model. Aiming at these problems, this paper presents an optimal error feedback recurrent convolutional neural network model. The error feedback layer structure in the model is strengthened according to the spatial and temporal characteristics of prediction error data.The model can deal with the error sequence with simple spatial relation. Through separating the history prediction error and training error, the training strategy is also improved to accelerate the convergence speed of the model. Experiments on traffic data of Beijing fourth ring road show that the optimized error feedback recurrent convolution neural network forecasting model can effectively improve the prediction accuracy, construction efficiency and robustness.

Key words: spatio-temporal data, traffic flow forecasting, convolution neural network, model optimization

YAO Si-jia, GUI Zhi-ming, GUO Li-min. Local Road Network Traffic Flow Prediction Based on Improved eRCNN[J]. Computer and Modernization, 2021, 0(07): 49-53.

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