Short-Term Forecasting of Bicycle Traffic Using Structural Time Series Models

Abstract

Short term forecasting algorithms are widely used for prediction of vehicular traffic flows for adaptive traffic management. However, despite the increasing interest in the promotion of cycling in cities, little research has been carried out into the use of traffic forecasting algorithms for bicycle traffic. Structural time series models allow the various components of a time series such as level, seasonal and regression effects to be modelled separately to allow analysis of previous trends and forecasting. In this paper, a case study at a segregated bicycle lane in Dublin, Ireland was performed to test the forecasting accuracy of structural time series models applied to continuous observations of cyclist traffic volumes. It has been shown that the proposed models can produce accurate peak period forecasts of cyclist traffic volumes at both 1 hour and fifteen minute resolution and that the percentage errors are lower for hourly forecasts. The inclusion of weather metrics as explanatory variables had varying effects on the forecasting accuracies of the models. These results directly aid the design of traffic signal control systems accommodating cyclists.