Forecasting of the Montreal Subway Smart Card Entry Logs with Event Data
This work addresses the need for transport operators to adapt supply scheduling and ticket availability to passenger demand, but it is incremental as it applies existing methods to new data with event integration.
The paper tackled the problem of long-term passenger demand forecasting for transport networks with fine-grained temporal resolution, using smart card and event data from Montreal, and found that generic data shaping enabled well-known regression models to achieve accurate predictions up to one year ahead with quarter-hour aggregation.
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 Montréal, Canada, that span a three-year period with two years for training and one year for testing.