Improving method of real-time offset tuning for arterial signal coordination using probe trajectory data

In the environment of intelligent transportation systems, traffic condition data would have higher resolution in time and space, which is especially valuable for managing the interrupted traffic at signalized intersections. There exist a lot of algorithms for offset tuning, but few of them take the...

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Main Authors: Jian Zhang, Yang Cheng, Shanglu He, Bin Ran
Format: Article
Language:English
Published: SAGE Publishing 2016-12-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814016683355
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spelling doaj-4cc0abe259c547d18c44800599ac16f42020-11-25T02:55:14ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402016-12-01910.1177/168781401668335510.1177_1687814016683355Improving method of real-time offset tuning for arterial signal coordination using probe trajectory dataJian Zhang0Yang Cheng1Shanglu He2Bin Ran3School of Transportation, Southeast University, Nanjing, ChinaResearch Center for Internet of Mobility, Southeast University, Nanjing, ChinaResearch Center for Internet of Mobility, School of Transportation, Southeast University, Nanjing, Jiangsu, ChinaSchool of Transportation, Southeast University, Nanjing, ChinaIn the environment of intelligent transportation systems, traffic condition data would have higher resolution in time and space, which is especially valuable for managing the interrupted traffic at signalized intersections. There exist a lot of algorithms for offset tuning, but few of them take the advantage of modern traffic detection methods such as probe vehicle data. This study proposes a method using probe trajectory data to optimize and adjust offsets in real time. The critical point, representing the changing vehicle dynamics, is first defined as the basis of this approach. Using the critical points related to different states of traffic conditions, such as free flow, queue formation, and dissipation, various traffic status parameters can be estimated, including actual travel speed, queue dissipation rate, and standing queue length. The offset can then be adjusted on a cycle-by-cycle basis. The performance of this approach is evaluated using a simulation network. The results show that the trajectory-based approach can reduce travel time of the coordinated traffic flow when compared with using well-defined offline offset.https://doi.org/10.1177/1687814016683355
collection DOAJ
language English
format Article
sources DOAJ
author Jian Zhang
Yang Cheng
Shanglu He
Bin Ran
spellingShingle Jian Zhang
Yang Cheng
Shanglu He
Bin Ran
Improving method of real-time offset tuning for arterial signal coordination using probe trajectory data
Advances in Mechanical Engineering
author_facet Jian Zhang
Yang Cheng
Shanglu He
Bin Ran
author_sort Jian Zhang
title Improving method of real-time offset tuning for arterial signal coordination using probe trajectory data
title_short Improving method of real-time offset tuning for arterial signal coordination using probe trajectory data
title_full Improving method of real-time offset tuning for arterial signal coordination using probe trajectory data
title_fullStr Improving method of real-time offset tuning for arterial signal coordination using probe trajectory data
title_full_unstemmed Improving method of real-time offset tuning for arterial signal coordination using probe trajectory data
title_sort improving method of real-time offset tuning for arterial signal coordination using probe trajectory data
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8140
publishDate 2016-12-01
description In the environment of intelligent transportation systems, traffic condition data would have higher resolution in time and space, which is especially valuable for managing the interrupted traffic at signalized intersections. There exist a lot of algorithms for offset tuning, but few of them take the advantage of modern traffic detection methods such as probe vehicle data. This study proposes a method using probe trajectory data to optimize and adjust offsets in real time. The critical point, representing the changing vehicle dynamics, is first defined as the basis of this approach. Using the critical points related to different states of traffic conditions, such as free flow, queue formation, and dissipation, various traffic status parameters can be estimated, including actual travel speed, queue dissipation rate, and standing queue length. The offset can then be adjusted on a cycle-by-cycle basis. The performance of this approach is evaluated using a simulation network. The results show that the trajectory-based approach can reduce travel time of the coordinated traffic flow when compared with using well-defined offline offset.
url https://doi.org/10.1177/1687814016683355
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AT yangcheng improvingmethodofrealtimeoffsettuningforarterialsignalcoordinationusingprobetrajectorydata
AT shangluhe improvingmethodofrealtimeoffsettuningforarterialsignalcoordinationusingprobetrajectorydata
AT binran improvingmethodofrealtimeoffsettuningforarterialsignalcoordinationusingprobetrajectorydata
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