LAUSR

Vehicle trajectories can provide a clear picture of traffic flow, which facilitates traffic state estimation and signal control optimization at intersections. Connected and Automated Vehicles (CAVs) can not only report their own trajectories, but also continuously collect surrounding vehicles’ trajectories using onboard sensors, which creates an opportunity to reconstruct fully-sampled vehicle trajectories. However, this data source brings challenges such as low penetration rate of CAVs and complex detection environment at intersections. To address these problems, this study proposes a novel framework under micro-perspective, in which trajectory estimation and fusion algorithms are integrated. The spatiotemporal correlations of detected trajectories are analyzed and classified into four regions, and four corresponding trajectory estimation algorithms based on extended car-following model are established to estimate the undetected part of each trajectory. Furthermore, a trajectory fusion algorithm based on Particle Filter is developed to fuse the estimated trajectories separately derived from upstream and downstream with minimized errors. The proposed method was comprehensively evaluated at field and simulated signalized intersections. The results show that compared with Variational Theory method, queue location error, time error and cumulative distance error of the proposed method were 76.3%, 44.4% and 54.5% lower, respectively, and the proposed trajectory fusion algorithm improved the accuracy and smoothness with the above three indices decreased by 17.3%, 47.7% and 6.2%, respectively. It was also found that the proposed method can adapt to different traffic conditions and penetration rates of CAVs.