Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network

Abstract

In order to explore a mobile virtual reality railway traffic congestion prediction algorithm based on convolutional neural network, an expanded causal convolution neural network (DCFCN) was proposed, which introduced the expanded convolution to increase the size of the receptive field and obtain the long-term memory of the sequence. At the same time, causal convolution is introduced to solve the problem of information leakage. DCFCN is made up of 6 convolutional layers, each layer achieves causal convolution through padding, and the expansion coefficient increases exponentially layer by layer. Experimental results show that LSTM and GRU can obtain the time sequence relationship of mobile virtual reality traffic flow sequence, and the prediction effect is better than simple method and traditional ARIMA model, but still inferior to DCFCN. The RMSE of DCFCN decreased by 0.38 compared with single-layer LSTM, 0.52 compared with double-layer LSTM, and 0.38 compared with single-layer and double-layer GRU. It shows that TCN model can indeed do better than RNN in sequence modeling. It is proved that the proposed DCFCN is superior to other comparison models in mobile virtual reality traffic flow prediction, and the computational efficiency on GPU is significantly improved.