Using LSTM and GRU neural network methods for traffic flow prediction

Accurate and real-time traffic flow prediction is important in Intelligent Transportation System (ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear models and cannot describe the stochastic and nonlinear nature of traffic flow. In recent years, deep-learning-based methods have been applied as novel alternatives for traffic flow prediction. However, which kind of deep neural networks is the most appropriate model for traffic flow prediction remains unsolved. In this paper, we use Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) neural network (NN) methods to predict short-term traffic flow, and experiments demonstrate that Recurrent Neural Network (RNN) based deep learning methods such as LSTM and GRU perform better than auto regressive integrated moving average (ARIMA) model. To the best of our knowledge, this is the first time that GRU is applied to traffic flow prediction.

I. Introduction

Over the past few years, ITS has been an important part of smart city and deployed much more than before. As a significant role in ITS, traffic flow information, especially short-term traffic flow information is strongly needed for individual travelers and business companies. However, accurate and real-time traffic flow prediction remains challenging for many decades, due to its stochastic and nonlinear nature. Existing methods mainly use linear models and shallow machine learning models to predict the incoming traffic flow and cannot describe the non-linearity and uncertainty well.