Bayesian neural networks for the prediction of stochastic travel times in urban networks

Urban travel time prediction has received much less attention than predictions on freeways, perhaps because urban travel times show much larger variations and are therefore much harder to predict. However, urban travel time can form a substantial part of the total travel time of a road user and therefore effort should be taken to predict urban travel times. In this study, neural networks are used for urban travel time prediction because these have shown to be able to deal with noisy data. Bayesian techniques are used for training of the networks, resulting in committees with lower error and in confidence bounds. It is shown that the neural network committees are capable of predicting the `low-frequency trend`, which can be seen when the high-frequency component of travel time is removed using de-noising. The errors of the predictions on the low-frequency trend are in the same order as when predicting freeway travel times, and it is shown that the predicted confidence bounds are accurate.