  Public special events, like sports games, concerts and festivals are well
known to create disruptions in transportation systems, often catching the
operators by surprise. Although these are usually planned well in advance,
their impact is difficult to predict, even when organisers and transportation
operators coordinate. The problem highly increases when several events happen
concurrently. To solve these problems, costly processes, heavily reliant on
manual search and personal experience, are usual practice in large cities like
Singapore, London or Tokyo. This paper presents a Bayesian additive model with
Gaussian process components that combines smart card records from public
transport with context information about events that is continuously mined from
the Web. We develop an efficient approximate inference algorithm using
expectation propagation, which allows us to predict the total number of public
transportation trips to the special event areas, thereby contributing to a more
adaptive transportation system. Furthermore, for multiple concurrent event
scenarios, the proposed algorithm is able to disaggregate gross trip counts
into their most likely components related to specific events and routine
behavior. Using real data from Singapore, we show that the presented model
outperforms the best baseline model by up to 26% in R2 and also has explanatory
power for its individual components.
