Exploiting Recurrent Neural Networks for Gate Traffic Prediction

Abstract

Traffic information plays a significant role in everyday activities. It can be used in the context of smart traffic management for detecting traffic congestions, incidents and other critical events. While there are numerous ways for drivers to find out where there is a traffic jam at a given moment, the estimation of the future traffic is not used for proactive activities such as ensuring a smoother traffic flow and to be prepared for critical situations. Therefore traffic prediction is focal both for public administrations and for the Police Force in order to do resource management, network security and to improve transportation infrastructure planning. A number of models and algorithms were applied to traffic prediction and achieved good results. Many of them require the length of past data to be predefined and static, do not take into account dynamic time lags and temporal autocorrelation. To address these issues in this paper we explore the usage of Artificial Neural Networks. We show how Long Short-Term Memory (LSTM), a particular type of Recurrent Neural Network (RNN), can overcome the above described issues. We compare LSTM with a standard Feed Forward Neural Network (FDNN), showing that the proposed model achieves higher accuracy and generalises well.