Passenger Flow Prediction based on Recurrent Neural Networks and Wavelet Transform

Abstract

It is a great significance for the accurate and real-time prediction of passenger flow in rail transit operation. In the process of passenger flow prediction, the new method of recurrent neural networks (RNNs) can well solve the problems of randomness and nonlinearity which can not be solved by the existed linear models. In this paper, the long short-term memory (LSTM) and the gated recurrent unit (GRU) networks, which are methods of RNNs, are employed to predict the dayparting passenger flow and the raw passenger flow data is denoised by the wavelet transform. Experimental results show that LSTM and GRU networks can well predict the passenger flow. And compared to LSTM, GRU is better for passenger flow prediction.