Study on Subway passenger flow prediction based on deep recurrent neural network

Abstract

As the construction and management of subway transit system becomes increasingly mature, analyzing the passenger flow information of the normal transportation network and accurately predicting the passenger flow in a short time have become the core of subway transit system operation and management. However, it is difficult for traditional intelligent prediction algorithms to meet the high accuracy and fast response capabilities required for predicting passenger flow in a short time in unexpected situations. In order to improve the prediction performance, this paper proposes a time series prediction model based on deep recurrent neural network (DRNN). Using DRNN’s unique memory function to capture the dynamic information of the time series, we can better learn the “trend” between data at different moments, so that we can more accurately predict the output at the next moment. The comparison among the case studies based on the measured data of subway passenger flow with time series characteristics, the traditional support vector machine and the neural network method, shows that DRNN prediction has the smallest overall deviation, small deviation fluctuation and good robustness.