A comparison between artificial neural networks and ARIMA models in traffic forecasting

Abstract

Motivation: Traffic forecasting is becoming a vital component of our travel experience. It plays a key role in intelligent transportation systems that allow us to make smarter use of existing transportation networks. This study focuses on the possible role of artificial neural networks in these systems and what data can be best feed in to them to retrieve the best results. Aim: The goal of this study is to see whether two layered feed forward neural networks outperform the statistical ARIMA model in motorway traffic forecasting. In specific, whether or not the usage of upstream and multivariate data decreases the forecasting errors of the neural network, how this relates to the amount of samples used as input, and how this relates to the amount of time steps that is forecasted ahead. Results and conclusions: Two different traffic networks are used to train and test the models. The testing results show that, when doing predictions using time steps covering 10 minutes of traffic data and forecasting one time step ahead, the optimal amount of samples used as input is 4. Increasing the input length after this does not result in better predictions, it even slightly increases the prediction errors. Moreover, it became clear that up to 3 or 4 time steps forecasting in the future, the neural networks using upstream data outperform the ARIMA model. After this an ARIMA model that uses deseasonalized data or a neural network that uses deseasonalized data is a better option. There is always a two layered neural network that outperforms the ARIMA models. Furthermore, the usage of upstream data almost always decreases the prediction errors. This is different with the usage of multivariate data, which hardly contributes to a better prediction in the used form.