  Public transportation system commuters are often interested in getting
accurate travel time information to plan their daily activities. However, this
information is often difficult to predict accurately due to the irregularities
of road traffic, caused by factors such as weather conditions, road accidents,
and traffic jams. In this study, two neural network models namely
multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for
predicting link travel time of a busy route with input generated using
Origin-Destination travel time matrix derived from a historical GPS dataset.
The experiment result showed that both models can make near-accurate
predictions however, LSTM is more susceptible to noise as time step increases.
