A regression spline based approach to enhance the prediction accuracy of bicycle counter data

Abstract: Regression analysis has been used in previous research to predict the number of bicycles registered by a bicycle counter. An important step to improve the prediction is to include a long-term trend curve estimate as part of the formulation of the regression target variable. In this way, it is possible to use the deviation from the trend curve estimate instead of the absolute number of bicycles as target variable in the regression problem formulation. This can help capturing the factors that are difficult, or even impossible, to model as input variables in the regression model, for example, larger infrastructural changes. This study aims to evaluate a regression spline-based approach to enhance the prediction accuracy of bicycle counter data. This will be achieved by formulating a regression problem, generating trend curve estimates using regression splines, and evaluating the resulted curves using cross validation on a set of chosen regression algorithms. We illustrate our approach by applying it on a time series recorded by a bicycle counter in Malmö city, Sweden. For the considered data set, our experimental results show that the spline trend curve estimate with knots between 12-19, which has been fitted to the time series, gives the best prediction. It also shows that the use of ensemble methods leads to better prediction, where the G.B. Regressor shows best performance with 19 knots.