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The rapid development of connected vehicle technology and the emergence ofride-hailing services have enabled the collection of a tremendous amount ofprobe vehicle trajectory data. Due to the large scale, the trajectory data havebecome a potential substitute for the widely used fixed-location sensors interms of the performance measures of transportation networks. Specifically, fortraffic volume and queue length estimation, most of the trajectory data basedmethods in the existing literature either require high market penetration ofthe probe vehicles to identify the shockwave or require the prior informationabout the queue length distribution and the penetration rate, which may not befeasible in the real world. To overcome the limitations of the existingmethods, this paper proposes a series of novel methods based on probabilitytheory. By exploiting the stopping positions of the probe vehicles in thequeues, the proposed methods try to establish and solve a single-variableequation for the penetration rate of the probe vehicles. Once the penetrationrate is obtained, it can be used to project the total queue length and thetotal traffic volume. The validation results using both simulation data andreal-world data show that the methods would be accurate enough for assistancein performance measures and traffic signal control at intersections, even whenthe penetration rate of the probe vehicles is very low.

Various methods for queue length and traffic volume estimation using probe vehicle trajectories