CTM Based Real-Time Queue Length Estimation at Signalized Intersection

Queue length is an important index of the efficiency of urban transport system. The traditional approaches seem insufficient for the estimation of the queue length when the traffic state fluctuates greatly. In this paper, the problem is solved by introducing the Cell Transmission Model, a macroscopic traffic flow, to describe the vehicles aggregation and discharging process at a signalized intersection. To apply the model to urban traffic appropriately, some of its rules were improved accordingly. Besides, we can estimate the density of each cell of the road in a short time interval. We, first, identify the cell, where the tail of the queue is located. Then, we calculate the exact location of the rear of the queue. The models are evaluated by comparing the estimated maximum queue length and average queue length with the results of simulation calibrated by field data and testing of queue tail trajectories. The results show that the proposed model can estimate the maximum and average queue length, as well as the real-time queue length with satisfactory accuracy.