A Reinforcement Learning-Based Distributed Control Scheme for Cooperative Intersection Traffic Control

Guzmán, José Antonio Silva; Pizarro, Germán; Nüñez, Felipe

doi:10.1109/access.2023.3283218

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Each decision is made by selecting an action from the set of actions as an output value and selecting the duration corresponding to the decision action as a way to change the signal light. Equation (1), which contains a middle action value, is used to compute the phase green light duration. When the choice action is less than the middle value, the computed duration is reduced by that many seconds relative to the time remaining in the preceding phase.…”

Section: Action Spacementioning

confidence: 99%

“…The government is under enormous strain and faces significant issues due to the limited road resources in cities that cause an imbalance between supply and demand for traffic. For example, a jammed-up traffic situation results in delays and higher fuel usage, both of which negatively impact the economy [1]. Additionally, many harmful gases are released, harming the environment globally and contributing to climate change [2].…”

Section: Introductionmentioning

confidence: 99%

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Improved Deep Reinforcement Learning for Intelligent Traffic Signal Control Using ECA_LSTM Network

Zai,

Yang

2023

Sustainability

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Reinforcement learning is one of the most widely used methods for traffic signal control, but the method experiences issues with state information explosion, inadequate adaptability to special scenarios, and low security. Therefore, this paper proposes a traffic signal control method based on the efficient channel attention mechanism (ECA-NET), long short-term memory (LSTM), and double Dueling deep Q-network (D3QN), which is EL_D3QN. Firstly, the ECA-NET and LSTM module are included in order to lessen the state space’s design complexity, improve the model’s robustness, and adapt to various emergent scenarios. As a result, the cumulative reward is improved by 27.9%, and the average queue length, average waiting time, and CO2 emissions are decreased by 15.8%, 22.6%, and 4.1%, respectively. Next, the dynamic phase interval tgap is employed to enable the model to handle more traffic conditions. Its cumulative reward is increased by 34.2%, and the average queue length, average waiting time, and CO2 emissions are reduced by 19.8%, 30.1%, and 5.6%. Finally, experiments are carried out using various vehicle circumstances and unique scenarios. In a complex environment, EL_D3QN reduces the average queue length, average waiting time, and CO2 emissions by at least 13.2%, 20.2%, and 3.2% compared to the four existing methods. EL_D3QN also exhibits good generalization and control performance when exposed to the traffic scenarios of unequal stability and equal stability. Furthermore, even when dealing with unique events like a traffic surge, EL_D3QN maintains significant robustness.

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Section: Action Spacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Deep Reinforcement Learning for Intelligent Traffic Signal Control Using ECA_LSTM Network

Zai,

Yang

2023

Sustainability

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“…This has spurred the development of distributed-control intersection networks, which offer a more adaptive and responsive approach to traffic management. In these networks, control decisions are distributed across multiple entities, allowing realtime adjustments based on traffic conditions, thereby improving overall traffic flow and reducing congestion [2].…”

Section: Introductionmentioning

confidence: 99%

A Blockchain-based Method Ensuring Integrity of Shared Data in a Distributed-Control Intersection Network

Ghazouani,

Ikidid,

Zaouiat

et al. 2023

IJACSA

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In modern urban transportation systems, the efficient management of traffic intersections is crucial to ensure smooth traffic flow and reduce congestion. Distributed-control intersection networks, where control decisions are made collaboratively by multiple entities, offer promising solutions. However, maintaining the security and the integrity of shared data among these entities poses significant challenges, including the risk of data tampering and unauthorized modifications. This paper proposes a novel approach that leverages blockchain technology to address these integrity concerns based on intelligent agents. By utilizing the decentralized and transparent nature of blockchain, our method ensures the authenticity and immutability of shared data within the distributed-control intersection network. The paper presents a detailed architecture, highlighting the integration of blockchain into the existing infrastructure, and discusses the benefits of this approach in enhancing data integrity, trust, and overall system reliability. Through a case study and simulation results, the proposed approach demonstrates its effectiveness in maintaining the integrity of shared data, thereby contributing to the advancement of secure and efficient traffic management systems.

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Analysis and implementation of computer network graph based on iterative control algorithm theory

Zhang,

Rong,

Zhang

et al. 2023

Soft Comput

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A Reinforcement Learning-Based Distributed Control Scheme for Cooperative Intersection Traffic Control

Cited by 9 publications

References 16 publications

Improved Deep Reinforcement Learning for Intelligent Traffic Signal Control Using ECA_LSTM Network

Improved Deep Reinforcement Learning for Intelligent Traffic Signal Control Using ECA_LSTM Network

A Blockchain-based Method Ensuring Integrity of Shared Data in a Distributed-Control Intersection Network

Analysis and implementation of computer network graph based on iterative control algorithm theory

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