Source 1primary paper2024Vehicles
Toolkit/reinforcement learning scheme based on VLC queuing/request/response behaviors
reinforcement learning scheme based on VLC queuing/request/response behaviors
Also known as: reinforcement learning approach
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
This tool is a reinforcement learning-based computational method for multi-intersection traffic signal scheduling that incorporates visible light communication (VLC) queuing, request, and response behaviors. It is described as part of a traffic management system integrating VLC localization services with learning-driven signal control.
Usefulness & Problems
Why this is useful
The method is useful for decentralized, scalable traffic signal control in multi-intersection settings, where coordination must be achieved from communication-driven traffic state information. In SUMO multi-intersection simulations, the proposed system reduced waiting time and travel time.
Problem solved
It addresses the problem of scheduling traffic signals effectively across multiple urban intersections using information derived from VLC-based queuing, request, and response behaviors. The cited work specifically frames this as improving multi-intersection traffic management performance.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
decentralized traffic signal controldecentralized traffic signal controlreinforcement learningreinforcement learningvisible light communication-based queuing/request/response signalingvisible light communication-based queuing/request/response signalingTechniques
Computational DesignTarget processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationimplementation constraint: multi component delivery burdenoperating role: builderswitch architecture: multi component
Implementation is described at the system level as integration of VLC localization services with learning-driven traffic signal control for multi-intersection management. The supplied evidence does not specify model architecture, sensing hardware, communication protocol parameters, or software release details.
The available evidence is limited to a single 2024 source and simulation-based evaluation in SUMO. No details are provided here on the reinforcement learning algorithm, reward formulation, training stability, hardware deployment, or validation in real traffic environments.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The paper introduces a multi-intersection traffic management system that integrates visible light communication localization services with learning-driven traffic signal control.
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
Approval Evidence
1 source3 linked approval claimsfirst-pass slug reinforcement-learning-scheme-based-on-vlc-queuing-request-response-behaviors
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
Source:
method functionsupports
A reinforcement learning scheme based on VLC queuing, request, and response behaviors is used to schedule traffic signals effectively.
A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively.
Source:
performance improvementsupports
In a SUMO multi-intersection simulation assessment, the proposed system reduced waiting times and travel times.
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
Source:
system propertysupports
The results highlight that the proposed method is decentralized and scalable, especially in multi-intersection scenarios.
the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios
Source:
Comparisons
Source-backed strengths
The reported strength is effective traffic signal scheduling within a multi-intersection management framework that combines VLC localization services and reinforcement learning. The available evidence also indicates improved waiting times and travel times in SUMO simulation assessments.
Source:
An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times.
Compared with free-energy calculations
reinforcement learning scheme based on VLC queuing/request/response behaviors and free-energy calculations address a similar problem space.
Shared frame: same top-level item type
Relative tradeoffs: looks easier to implement in practice.
Compared with mathematical model
reinforcement learning scheme based on VLC queuing/request/response behaviors and mathematical model address a similar problem space.
Shared frame: same top-level item type
Compared with SwiftLib
reinforcement learning scheme based on VLC queuing/request/response behaviors and SwiftLib address a similar problem space.
Shared frame: same top-level item type
Relative tradeoffs: looks easier to implement in practice.
Ranked Citations
- 1.