Fusing probe speed and flow data for robust short-term congestion front forecasts

In this paper a robust and flexible method is proposed that combines the strengths of detector as well as Floating Car (FC) data in order to provide short-term congestion front forecasts. Based on the high spatio-temporal resolution of FC data, congested regimes and according congestion fronts are identified accurately. Subsequently, the flow data provided by loop detectors are utilized in order to predict these congestion fronts for a time horizon of up to ten minutes. Three variations of the method are presented which focus the difficulty of estimating traffic density in congested traffic conditions with given data. The evaluation is based on real FC as well as loop detector data collected during a congestion on the German Autobahn A9. Comparisons of the variants of the proposed method and a naive predictor emphasize the advantage of combining both data sources and point out the strategy that results in the most accurate front forecasts.