Exploiting probe data to estimate the queue profile in urban networks

Queues at signalized intersections are one of the main causes of traffic delays and urban traffic state variability. Hence, a method to estimate queue characteristics provides a better understanding of urban traffic dynamics and a performance measurement of signalized arterials. In order to capture the evolution of queues, we aim at leveraging the collective effect of spatially and temporally dispersed probe data to identify the formation and dissipation of queues in the time-space plane. The queue profile characterizes the evolution of both queue front and back, which consequently can be separated in a two-step estimation process resulting to the queue profile polygon. The evolution of queue front, in the time-space diagram, based on the kinematic traffic shockwave theory is modeled as a line with the known slope of queue-discharging shockwave and estimated with a constrained optimization and a technique known as support vector machine. The evolution of back of queue is more challenging and modeled as a piecewise linear function where slope of segments is between the queue-discharging shockwave and zero. In the proposed method, the input data consists of position and velocity of probe vehicles. The queue profile estimation method does not require any explicit information of signal settings and arrival distribution. The proposed method is tested with various penetration rates and sampling intervals of probe data, which reveals promising results once compared to a uniform arrival queue profile estimation procedure. The proposed method could be beneficial for spillback identification, vehicle trajectory construction, and fuel consumption and emission estimation.

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