Source 1primary paper2024Vehicles
Toolkit/learning-driven traffic signal control
learning-driven traffic signal control
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Learning-driven traffic signal control is an engineering method for multi-intersection traffic management that integrates visible light communication localization services with reinforcement learning-based signal scheduling. It uses VLC-derived queuing, request, and response behaviors to control traffic signals in simulated multi-intersection settings.
Usefulness & Problems
Why this is useful
This method is useful for coordinating traffic signals across multiple intersections using localization information derived from visible light communication. In SUMO multi-intersection simulations, the integrated system reduced vehicle waiting time and travel time.
Problem solved
It addresses the problem of scheduling traffic signals effectively in a multi-intersection network while incorporating localization-related traffic state information from VLC services. The cited work specifically frames this as improving urban intersection efficiency through integrated localization and learning-driven control.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete method used to build, optimize, or evolve an engineered system.
Mechanisms
decentralized traffic signal schedulingreinforcement learningvisible light communication-based localizationTechniques
No technique tags yet.
Target processes
localizationInput: Light
Implementation Constraints
The system is described as an integration of VLC localization services with learning-driven traffic signal control in a multi-intersection management framework. Practical implementation details beyond this integration, such as controller architecture, sensing hardware, communication protocol parameters, or deployment constraints, are not provided in the supplied evidence.
The evidence is limited to a single cited study and simulation-based validation in SUMO. No evidence is provided here for real-world deployment, algorithmic details, hardware requirements, or comparative performance against multiple alternative controllers.
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.
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 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 source2 linked approval claimsfirst-pass slug learning-driven-traffic-signal-control
By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established.
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 descriptionsupports
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.
Source:
Comparisons
Source-backed strengths
The reported strength is effective traffic signal scheduling based on reinforcement learning using VLC queuing, request, and response behaviors. The available evidence also indicates improved waiting time and travel time in a SUMO multi-intersection simulation assessment.
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.
Ranked Citations
- 1.