A convolutional neural network combined with a Gaussian process for speed prediction in traffic networks

This paper proposes a traffic speed prediction framework combining a Convolutional Neural Network (CNN) with a Gaussian Process (GP) and is an extension of ConvNetGP [1]. The main focus is on spatio-temporal large scale traffic networks and on uncertainty quantification. The emphasis is on the impact on the measurement noises on the predicted traffic speeds. The Gaussian Process regression provides a variance which characterises the accuracy of the prediction. The traffic speed data is converted into a three dimensional format like images and these are inputs of the CNN-GP framework for traffic networks. The CNN-GP framework provides 18.23% average improvement of the speed root mean square error compared with the generic CNN and gives a quantitative characterisation of the noise effects.