Piecewise Inverse Speed Correction by Using Individual Travel Times

Abstract

Traffic state estimation plays an important role in operational traffic management and is essential for real-time traffic modeling and prediction. As more heterogeneous traffic data [e.g., from loops, probe vehicles, advanced vehicle identification (AVI) systems] become available, data fusion has become one of the main challenges of state estimation. Given the different semantics over space and time (e.g., AVI data and local data from loops), data fusion is a far more complex problem than it appears at first glance. A new algorithm for fusing data from local detectors (loops) with travel times obtained from AVI systems was developed. This simple but mathematically elegant algorithm— called piecewise inverse speed correction—by using individual travel times (PISCIT) correctly and efficiently combines these data (in essence incompatible) and produces a state estimate (space mean speeds per cell), which is better than one obtained by any of the data sources individually. Moreover, PISCIT is robust with respect to structural and random errors in the source data. The approach is validated using synthetic data generated by microscopic simulation. The algorithm corrects the traffic state correctly even when nonuniform deviations are up to 70%.