Abstract
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.
Highlights
It has been generally recognized that the vehicular queue length is a crucial quantitative measure used in the evaluation of the performance of signalized intersections [1,2,3] and signal optimization [4,5,6,7,8,9]
The first type models are based on the construction of accumulation curves by means of analysis of vehicles inputoutput to a signal link, where vehicles between two curves are accounted for as being in a queue
We propose the M-Cell Transmission Model (CTM) based model to estimate the real-time queue length at a signalized intersection, instead of just calculating the maximum or average queue length as traditional methods often do
Summary
It has been generally recognized that the vehicular queue length is a crucial quantitative measure used in the evaluation of the performance of signalized intersections [1,2,3] and signal optimization [4,5,6,7,8,9]. The method was first demonstrated by Webster [4] and later improved by many scholars [5, 10,11,12]. Such models are widely used to calculate the queue length, it is hard to obtain the real-time queue length. The accumulation inputoutput curve is difficult to structure under complex traffic conditions. Such methods are limited in their ability to describe complex queuing processes
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