  One of the major goals of transport operators is to adapt the transport
supply scheduling to the passenger demand for existing transport networks
during each specific period. Another problem mentioned by operators is
accurately estimating the demand for disposable ticket or pass to adapt ticket
availability to passenger demand. In this context, we propose generic data
shaping, allowing the use of well-known regression models (basic, statistical
and machine learning models) for the long-term forecasting of passenger demand
with fine-grained temporal resolution. Specifically, this paper investigates
the forecasting until one year ahead of the number of passengers entering each
station of a transport network with a quarter-hour aggregation by taking
planned events into account (e.g., concerts, shows, and so forth). To compare
the models and the quality of the prediction, we use a real smart card and
event data set from the city of Montr\'eal, Canada, that span a three-year
period with two years for training and one year for testing.
