  Many factors can affect the predictability of public bus services such as
traffic, weather and local events. Other aspects, such as day of week or hour
of day, may influence bus travel times as well, either directly or in
conjunction with other variables. However, the exact nature of such
relationships between travel times and predictor variables is, in most
situations, not known. In this paper we develop a framework that allows for
flexible modeling of bus travel times through the use of Additive Models. In
particular, we model travel times as a sum of linear as well as nonlinear terms
that are modeled as smooth functions of predictor variables. The proposed class
of models provides a principled statistical framework that is highly flexible
in terms of model building. The experimental results demonstrate uniformly
superior performance of our best model as compared to previous prediction
methods when applied to a very large GPS data set obtained from buses operating
in the city of Rio de Janeiro.
