Regression-based evaluation of bicycle flow trend estimates

It has been shown in previous research that regression modeling can be used in order to predict the number of bicycles registered by a bicycle counter. To improve the prediction accuracy, it has also been suggested that a long-term trend curve estimate can be incorporated in a regression problem formulation. A long-term trend curve estimate aims to capture those factors that are difficult, or even impossible, to explicitly model as input variables in the regression model. In the current paper, we present a regression-based approach for evaluating long-term trend curve estimates regarding their possibility to improve the regression prediction accuracy of bicycle counter data. We illustrate our approach by applying it on a time series recorded by a bicycle counter in Malmo, Sweden. For the considered data set, our experimental results indicate that a polynomial of degree two, which has been fitted to the time series, gives the best prediction.