Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning

Work zone areas are frequent congested sections considered as the freeway bottleneck. Connected and autonomous vehicle (CAV) trajectory optimization can improve the operating efficiency in bottleneck areas by harmonizing vehicles’ manipulations. This study presents a joint trajectory optimization of...

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Autores principales:	Tong Zhu, Xiaohu Li, Wei Fan, Changshuai Wang, Haoxue Liu, Runqing Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Transportation engineering TA1001-1280 Transportation and communications HE1-9990
Acceso en línea:	https://doaj.org/article/e2e97a5be77d4336b48d20777a9a8414
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spelling	oai:doaj.org-article:e2e97a5be77d4336b48d20777a9a84142021-11-15T01:19:44ZTrajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning2042-319510.1155/2021/9805560https://doaj.org/article/e2e97a5be77d4336b48d20777a9a84142021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9805560https://doaj.org/toc/2042-3195Work zone areas are frequent congested sections considered as the freeway bottleneck. Connected and autonomous vehicle (CAV) trajectory optimization can improve the operating efficiency in bottleneck areas by harmonizing vehicles’ manipulations. This study presents a joint trajectory optimization of cooperative lane changing, merging, and car-following actions for CAV control at a local merging point together with upstream points. The multiagent reinforcement learning (MARL) method is applied in this system, with one agent providing a merging advisory service at the merging point and controlling the inner-lane vehicles’ headway for smooth outer-lane vehicle merging, while other agents provide lane-changing advisory services at advance lane-changing points to control how vehicles make lane changes in advance and perform corresponding headway adjustment, similar to and jointly with the merging advisory service. Uniting all agents, the coordination graph (CG) method is applied to seek the global optimum, overcoming the exponential growth problem in MARL. Using MATLAB and the VISSIM COM interface, an online simulation platform is established. The simulation results show that MARL is effective for online computation with in-timing response. More importantly, comparisons of the results obtained in various scenarios demonstrate that the proposed system obtained smoother vehicle trajectories in all controlled sections, rather than only in the merging area, indicating that it can achieve better traffic conditions in freeway work zone areas.Tong ZhuXiaohu LiWei FanChangshuai WangHaoxue LiuRunqing ZhaoHindawi-WileyarticleTransportation engineeringTA1001-1280Transportation and communicationsHE1-9990ENJournal of Advanced Transportation, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Transportation engineering TA1001-1280 Transportation and communications HE1-9990
spellingShingle	Transportation engineering TA1001-1280 Transportation and communications HE1-9990 Tong Zhu Xiaohu Li Wei Fan Changshuai Wang Haoxue Liu Runqing Zhao Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning
description	Work zone areas are frequent congested sections considered as the freeway bottleneck. Connected and autonomous vehicle (CAV) trajectory optimization can improve the operating efficiency in bottleneck areas by harmonizing vehicles’ manipulations. This study presents a joint trajectory optimization of cooperative lane changing, merging, and car-following actions for CAV control at a local merging point together with upstream points. The multiagent reinforcement learning (MARL) method is applied in this system, with one agent providing a merging advisory service at the merging point and controlling the inner-lane vehicles’ headway for smooth outer-lane vehicle merging, while other agents provide lane-changing advisory services at advance lane-changing points to control how vehicles make lane changes in advance and perform corresponding headway adjustment, similar to and jointly with the merging advisory service. Uniting all agents, the coordination graph (CG) method is applied to seek the global optimum, overcoming the exponential growth problem in MARL. Using MATLAB and the VISSIM COM interface, an online simulation platform is established. The simulation results show that MARL is effective for online computation with in-timing response. More importantly, comparisons of the results obtained in various scenarios demonstrate that the proposed system obtained smoother vehicle trajectories in all controlled sections, rather than only in the merging area, indicating that it can achieve better traffic conditions in freeway work zone areas.
format	article
author	Tong Zhu Xiaohu Li Wei Fan Changshuai Wang Haoxue Liu Runqing Zhao
author_facet	Tong Zhu Xiaohu Li Wei Fan Changshuai Wang Haoxue Liu Runqing Zhao
author_sort	Tong Zhu
title	Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning
title_short	Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning
title_full	Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning
title_fullStr	Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning
title_full_unstemmed	Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning
title_sort	trajectory optimization of cavs in freeway work zone considering car-following behaviors using online multiagent reinforcement learning
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/e2e97a5be77d4336b48d20777a9a8414
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Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning

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