Reconstructing the Traffic State by Fusion of Heterogeneous Data

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on the ‘adaptive smoothing method’ which takes as input stationary detector data as typically collected by traffic control centers. We generalize this method to allow for fusion with floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: (1) compensation for gaps in data caused by detector failure; (2) separation of noise from dynamic traffic information; and (3) the fusion of floating car data with stationary detector data.