Traffic Flow Forecast of Road Networks with Recurrent Neural Networks

TL;DR

This paper explores the use of various recurrent neural network architectures to predict traffic flow in urban road networks, aiming to improve smart city transportation systems despite data limitations.

Contribution

It evaluates different RNN models, especially vector output models with gated recurrent units, for traffic prediction using real sensor data.

Findings

Vector output models with gated recurrent units perform best.

Prediction accuracy is limited by small dataset size.

Recurrent neural networks can model complex traffic patterns.

Abstract

The interest in developing smart cities has increased dramatically in recent years. In this context an intelligent transportation system depicts a major topic. The forecast of traffic flow is indispensable for an efficient intelligent transportation system. The traffic flow forecast is a difficult task, due to its stochastic and non linear nature. Besides classical statistical methods, neural networks are a promising possibility to predict future traffic flow. In our work, this prediction is performed with various recurrent neural networks. These are trained on measurements of induction loops, which are placed in intersections of the city. We utilized data from beginning of January to the end of July in 2018. Each model incorporates sequences of the measured traffic flow from all sensors and predicts the future traffic flow for each sensor simultaneously. A variety of model architectures, forecast horizons and input data were investigated. Most often the vector output model with gated recurrent units achieved the smallest error on the test set over all considered prediction scenarios. Due to the small amount of data, generalization of the trained models is limited.