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 macroscopi...
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2015-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/328712 |
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doaj-e1a56d4391e84573a8eda6b06960d1ff2020-11-25T00:03:44ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/328712328712CTM Based Real-Time Queue Length Estimation at Signalized IntersectionShuzhi Zhao0Shidong Liang1Huasheng Liu2Minghui Ma3College of Transportation, Jilin University, Changchun 130022, ChinaCollege of Transportation, Jilin University, Changchun 130022, ChinaCollege of Transportation, Jilin University, Changchun 130022, ChinaCollege of Transportation, Jilin University, Changchun 130022, ChinaQueue 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.http://dx.doi.org/10.1155/2015/328712 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shuzhi Zhao Shidong Liang Huasheng Liu Minghui Ma |
spellingShingle |
Shuzhi Zhao Shidong Liang Huasheng Liu Minghui Ma CTM Based Real-Time Queue Length Estimation at Signalized Intersection Mathematical Problems in Engineering |
author_facet |
Shuzhi Zhao Shidong Liang Huasheng Liu Minghui Ma |
author_sort |
Shuzhi Zhao |
title |
CTM Based Real-Time Queue Length Estimation at Signalized Intersection |
title_short |
CTM Based Real-Time Queue Length Estimation at Signalized Intersection |
title_full |
CTM Based Real-Time Queue Length Estimation at Signalized Intersection |
title_fullStr |
CTM Based Real-Time Queue Length Estimation at Signalized Intersection |
title_full_unstemmed |
CTM Based Real-Time Queue Length Estimation at Signalized Intersection |
title_sort |
ctm based real-time queue length estimation at signalized intersection |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2015/328712 |
work_keys_str_mv |
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