  Accurate and reliable travel time predictions in public transport networks
are essential for delivering an attractive service that is able to compete with
other modes of transport in urban areas. The traditional application of this
information, where arrival and departure predictions are displayed on digital
boards, is highly visible in the city landscape of most modern metropolises.
More recently, the same information has become critical as input for
smart-phone trip planners in order to alert passengers about unreachable
connections, alternative route choices and prolonged travel times. More
sophisticated Intelligent Transport Systems (ITS) include the predictions of
connection assurance, i.e. to hold back services in case a connecting service
is delayed. In order to operate such systems, and to ensure the confidence of
passengers in the systems, the information provided must be accurate and
reliable. Traditional methods have trouble with this as congestion, and thus
travel time variability, increases in cities, consequently making travel time
predictions in urban areas a non-trivial task. This paper presents a system for
bus travel time prediction that leverages the non-static spatio-temporal
correlations present in urban bus networks, allowing the discovery of complex
patterns not captured by traditional methods. The underlying model is a
multi-output, multi-time-step, deep neural network that uses a combination of
convolutional and long short-term memory (LSTM) layers. The method is
empirically evaluated and compared to other popular approaches for link travel
time prediction and currently available services, including the currently
deployed model in Copenhagen, Denmark. We find that the proposed model
significantly outperforms all the other methods we compare with, and is able to
detect small irregular peaks in bus travel times very quickly.
