Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment

Queue length is a crucial measurement of traffic signal control at urban intersections. Conventional queue length estimation methods mostly still rely on fixed detectors. The development of connected vehicles (CV) provides massive amounts of vehicle trajectory data, and the queue length estimation b...

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Main Authors: Lu Wei, Jin-hong Li, Li-wen Xu, Lei Gao, Jian Yang
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2022/9568723
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author Lu Wei
Jin-hong Li
Li-wen Xu
Lei Gao
Jian Yang
author_facet Lu Wei
Jin-hong Li
Li-wen Xu
Lei Gao
Jian Yang
author_sort Lu Wei
collection DOAJ
description Queue length is a crucial measurement of traffic signal control at urban intersections. Conventional queue length estimation methods mostly still rely on fixed detectors. The development of connected vehicles (CV) provides massive amounts of vehicle trajectory data, and the queue length estimation based on CV data has received considerable attention in recent years. However, most existing CV-based methods require the prior knowledge of the penetration rate of CV and vehicle arrivals, but the estimation of these prior distributions has not been well studied. To address this issue, this paper proposes a cycle-based queue length estimation method under partially connected vehicle (CV) environment, with the prior vehicle arrivals being unknown. The empirical Bayes method is adopted to estimate the arrival rate by leveraging the observed queued CV information such as the number and positions. The hyperparameter estimation problem of the prior distribution is solved by the maximum likelihood estimation (MLE) method. To validate the proposed queue length estimation method, a simulation environment with partially connected vehicles is established using VISSIM and Python for data generating. The results in terms of normalized mean absolute errors (NMAE) and normalized root mean square errors (NRMSE) show that the proposed method could produce accurate and reliable estimated queue length under various CV penetration rates.
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institution Kabale University
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spelling doaj-art-6f0919b0b644465691647db338344dbb2025-02-03T05:53:49ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/9568723Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle EnvironmentLu Wei0Jin-hong Li1Li-wen Xu2Lei Gao3Jian Yang4Beijing Key Lab of Urban Road Traffic Intelligent Control TechnologyBeijing Key Lab of Urban Road Traffic Intelligent Control TechnologyCollege of ScienceBeijing Key Lab of Urban Road Traffic Intelligent Control TechnologyBeijing Key Lab of Urban Road Traffic Intelligent Control TechnologyQueue length is a crucial measurement of traffic signal control at urban intersections. Conventional queue length estimation methods mostly still rely on fixed detectors. The development of connected vehicles (CV) provides massive amounts of vehicle trajectory data, and the queue length estimation based on CV data has received considerable attention in recent years. However, most existing CV-based methods require the prior knowledge of the penetration rate of CV and vehicle arrivals, but the estimation of these prior distributions has not been well studied. To address this issue, this paper proposes a cycle-based queue length estimation method under partially connected vehicle (CV) environment, with the prior vehicle arrivals being unknown. The empirical Bayes method is adopted to estimate the arrival rate by leveraging the observed queued CV information such as the number and positions. The hyperparameter estimation problem of the prior distribution is solved by the maximum likelihood estimation (MLE) method. To validate the proposed queue length estimation method, a simulation environment with partially connected vehicles is established using VISSIM and Python for data generating. The results in terms of normalized mean absolute errors (NMAE) and normalized root mean square errors (NRMSE) show that the proposed method could produce accurate and reliable estimated queue length under various CV penetration rates.http://dx.doi.org/10.1155/2022/9568723
spellingShingle Lu Wei
Jin-hong Li
Li-wen Xu
Lei Gao
Jian Yang
Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment
Journal of Advanced Transportation
title Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment
title_full Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment
title_fullStr Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment
title_full_unstemmed Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment
title_short Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment
title_sort queue length estimation for signalized intersections under partially connected vehicle environment
url http://dx.doi.org/10.1155/2022/9568723
work_keys_str_mv AT luwei queuelengthestimationforsignalizedintersectionsunderpartiallyconnectedvehicleenvironment
AT jinhongli queuelengthestimationforsignalizedintersectionsunderpartiallyconnectedvehicleenvironment
AT liwenxu queuelengthestimationforsignalizedintersectionsunderpartiallyconnectedvehicleenvironment
AT leigao queuelengthestimationforsignalizedintersectionsunderpartiallyconnectedvehicleenvironment
AT jianyang queuelengthestimationforsignalizedintersectionsunderpartiallyconnectedvehicleenvironment