Modelling of a Stochastic Spatiotemporal Variable in Transport Domain
In this article, building on our previous work, we engage in spatiotemporal modelling of transport demand in the Montreal metropolitan area over the period of six years. We employ classical machine learning and regression models, which predict bike-sharing demand in the form of daily cumulative sums of bike trips for each considered docking station. Hourly estimates of demand are then determined by considering the statistical distribution of demand across individual hours of an average day. In order to capture seasonal and other regular variation of demand, longer-term distribution characteristics of bike trips, such as their average number falling on each day of the week, month of the year, etc., were also used as input attributes. We initially conjectured that weather would be an important source of irregular variation in bike-sharing demand, and subsequently included several available meteorological variables in our models. We validated our models by Hold-Out and 10-Fold Cross-Validation, with encouraging results.