A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; Ukkusuri, Satish V.

doi:10.1016/j.trc.2014.12.009

Cited by 61 publications

(30 citation statements)

References 31 publications

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“…us, it only provides an approximate inference of the exact message being passed. Zhu et al [16] first proposed the JTA instead of the max-plus algorithm to obtain the best joint action for traffic signals and to realize network-wide signal coordination. JTA was first proposed by Jensen et al [17].…”

Section: Review Of the Literature On Signal Coordinationmentioning

confidence: 99%

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Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination

Zhao

Shen

et al. 2020

Journal of Advanced Transportation

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is study develops three measures to optimize the junction-tree-based reinforcement learning (RL) algorithm, which will be used for network-wide signal coordination. e first measure is to optimize the frequency of running the junction-tree algorithm (JTA) and the intersection status division. e second one is to optimize the JTA information transmission mode. e third one is to optimize the operation of a single intersection. A test network and three test groups are built to analyze the optimization effect. Group 1 is the control group, group 2 adopts the optimizations for the basic parameters and the information transmission mode, and group 3 adopts optimizations for the operation of a single intersection. Environments with different congestion levels are also tested. Results show that optimizations of the basic parameters and the information transmission mode can improve the system efficiency and the flexibility of the green light, and optimizing the operation of a single intersection can improve the efficiency of both the system and the individual intersection. By applying the proposed optimizations to the existing JTA-based RL algorithm, network-wide signal coordination can perform better.

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Section: Review Of the Literature On Signal Coordinationmentioning

confidence: 99%

“…Zhu et al [16] demonstrated that the test network can perform better under the JTA compared to an adaptive or single-agent RLbased control. Although the network system improved, some intersections still experienced poor operations.…”

Section: Motivations and Contributions Of This Studymentioning

confidence: 99%

Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination

Zhao

Shen

et al. 2020

Journal of Advanced Transportation

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“…For instance, Zhu et al [19] present a learning algorithm that optimizes traffic control. In particular, their algorithm is executed by means of an ABS.…”

Section: Learning Algorithms In Abssmentioning

confidence: 99%

ATABS: A technique for automatically training agent-based simulators

García‐Magariño

Palacios-Navarro

2016

Simulation Modelling Practice and Theory

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“…Ella M. Atkins is with the Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, 48109 USA e-mail: ematkins@umich.edu (see https://aero.engin.umich.edu/people/ella-atkins/). management [4], formation flight [5], and connected vehicle control [6]. Virtual structures [7], [8], consensus [9]- [11], containment control [12]- [14], partial-differential equation (PDE) based approaches [15]- [17], continuum deformation [18], [19], and graph rigidity methods [20], [21] are available multiagent coordination approaches.…”

Section: Introductionmentioning

confidence: 99%

Physics-Based Freely Scalable Continuum Deformation for UAS Traffic Coordination

Rastgoftar

Atkins

2020

IEEE Trans. Control Netw. Syst.

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This paper develops a novel physics-inspired traffic coordination approach and applies it to Unmanned Aircraft System (UAS) traffic management. We extend available physicsinspired approaches previously applied to 1-D traffic flow on highways and urban streets to support models of traffic coordination in higher dimension airspace for cases where no predefined paths exist. The paper considers airspace as a finite control volume while UAS coordination, treated as continuum deformation, is controlled at the airspace boundaries. By partitioning airspace into planned and unplanned spaces, the paper models nominal coordination in the planned airspace as the solution of a partial differential equation with spatiotemporal parameters. This paper also improves resilience to vehicle failures with a resilient boundary control algorithm to update the geometry of the planned space when UAS problems threaten safe coordination in existing navigable airspace channels. To support UAS coordination at the microscopic level, we propose clustering vehicles based on vehicle performance limits. UAS clusters, with each UAS treated as a particle of a virtual rigid body, use leader-follower containment to acquire the macroscopic desired trajectory.

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A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

Cited by 61 publications

References 31 publications

Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination

Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination

ATABS: A technique for automatically training agent-based simulators

Physics-Based Freely Scalable Continuum Deformation for UAS Traffic Coordination

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