Calibration-free traffic state estimation method using single detector and connected vehicles with Kalman filtering and RTS smoothing

Abstract

Traffic state estimation (TSE), which reconstructs complete traffic states from partial observation data, is an essential component in intelligent transportation systems. In this study, a novel traffic state estimation method using connected vehicles and a single detector based on Kalman filtering and Rauch–Tung–Striebel (RTS) smoothing is proposed. To the author’s knowledge, while filtering is common approach for TSE, smoothing has not been employed to TSE in the literature. The important features of the proposed method are twofold. First, thanks to RTS smoothing, it can estimate accurate traffic state using a single detector, and it does not require detectors in every entries and exits of a road section. In addition, the estimation accuracy is not significantly sensitive to detector location. Second, it does not require parameter calibration thanks to the method’s data-driven nature. These features will make the method flexibly applicable for practical conditions. Estimation accuracy of the proposed method was empirically evaluated by using actual vehicle trajectories data, and the effectiveness of the above two features was confirmed.