Deep Learning for Short-term Traffic Conditions Prediction

Abstract

The development of intelligent transportation systems usually needs to predict the traffic conditions under a large data volume. Existing approaches usually use a single source of data and the impacts of the neighborhood road sections are not concerned. As a result, their prediction accuracy is usually compromised. To address this issue, we propose a recurrent neural network to predict the road conditions simultaneously concerning the information of multiple road sections at the same time. By perceiving the connectivity between multiple road sections and capturing their mutual influence, our model can significantly improve the prediction accuracy. The experiments based on two real-life dataset shows that our model outperforms the baseline model.