Collaborative Control Method for Transit Signal Priority Using Cooperative Game Theory and Reinforcement Learning

المفاهيم الأساسية

A novel eight-phase priority signal control method is introduced, leveraging a hybrid decision-making framework that integrates cooperative game theory and reinforcement learning to effectively address the multi-objective challenges in transit signal priority control.

الملخص

The paper proposes a novel eight-phase priority signal control method, CBQL-TSP, that integrates cooperative game theory and reinforcement learning to address the challenges in transit signal priority (TSP) control.
The key highlights are:

The TSP problem is modeled as a multi-agent, multi-action space Markov decision process, where each agent represents a set of traffic signals (priority or non-priority signals).

A cooperative game model is used to differentiate between priority and non-priority signal phases, with the Shapley value function quantifying the marginal contributions of each participant.

The CBQL algorithm, a dynamic learning approach, adjusts the signal control strategies for both priority and non-priority vehicles to achieve a balance of contributions and an optimal solution within the game-theoretic framework.

The eight-phase priority signal control method is designed to enhance the adaptability and responsiveness of the system to real-time traffic conditions.

Simulation experiments on a real-world road network demonstrate that the proposed CBQL-TSP method outperforms conventional TSP control methods in terms of network stability, average travel time, and bus travel time, particularly under high private vehicle demand.

The research introduces a novel approach to TSP control by leveraging the synergies between cooperative game theory and reinforcement learning, enabling dynamic and adaptive signal control to optimize the efficiency and reliability of both priority and non-priority vehicles.

الإحصائيات

The average travel time for buses in the city center is approximately 468.22 seconds with the CBQL-TSP method, compared to 747.93 seconds with the CBQL-noTSP method, indicating a 37.40% reduction.
Compared to MB-TSP, ASC-TSP, and another iteration of MB-TSP, the CBQL-TSP method reduces the average bus transit time in the city center by approximately 11.46%.

اقتباسات

"The CBQL-TSP method reduces the average bus transit time in the city center by about 37.40% compared to the CBQL-noTSP method."
"Compared to MB-TSP, ASC-TSP, and another iteration of MB-TSP, the CBQL-TSP method reduces the average bus transit time in the city center by approximately 11.46%."

الرؤى الأساسية المستخلصة من

Collaborative Control Method of Transit Signal Priority Based on Cooperative Game and Reinforcement Learning

by Hao Qin,Weis... في arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19683.pdf

استفسارات أعمق

What other transportation modes or stakeholders could be incorporated into the cooperative game-theoretic framework to further optimize the overall transportation network efficiency

Incorporating additional transportation modes or stakeholders into the cooperative game-theoretic framework can further enhance the optimization of the overall transportation network efficiency. One key stakeholder that could be integrated is pedestrians. By considering pedestrian movement patterns and priorities at intersections, the cooperative game model can allocate signal timings to ensure safe and efficient pedestrian crossings. This inclusion can help reduce conflicts between pedestrians and vehicles, improving overall traffic flow and safety.
Another transportation mode that could be incorporated is cycling. By including cyclists in the decision-making process, the cooperative game model can optimize signal timings to accommodate bike lanes, reduce cyclist-vehicle conflicts, and promote sustainable transportation options. This integration can lead to a more holistic approach to transportation network management, catering to the needs of cyclists and enhancing the overall efficiency of the system.
Furthermore, the inclusion of emergency vehicles in the cooperative game-theoretic framework can prioritize their movement through intersections, ensuring timely responses to emergencies. By giving emergency vehicles signal priority based on real-time conditions, the transportation network can better support emergency services and improve overall emergency response times.

How could the CBQL-TSP method be extended to handle more complex traffic scenarios, such as intersections with multiple priority vehicle types or dynamic traffic patterns

To handle more complex traffic scenarios, such as intersections with multiple priority vehicle types or dynamic traffic patterns, the CBQL-TSP method can be extended in several ways:

Dynamic Priority Assignment: Implement a dynamic priority assignment mechanism that can adapt to changing traffic conditions and prioritize different types of vehicles based on real-time demands. This flexibility can ensure efficient traffic flow for various vehicle types without compromising overall network performance.

Multi-Agent Collaboration: Enhance the cooperative game model to accommodate multiple types of priority vehicles and stakeholders. By considering the interactions between buses, emergency vehicles, cyclists, and pedestrians, the decision-making framework can optimize signal timings to cater to the diverse needs of different road users.

Machine Learning Integration: Integrate machine learning algorithms to analyze and predict traffic patterns, enabling the system to make proactive adjustments to signal timings. By leveraging machine learning, the CBQL-TSP method can adapt to complex scenarios and optimize traffic control strategies in real time.

Adaptive Signal Control: Develop adaptive signal control strategies that can dynamically adjust signal timings based on the current traffic conditions. By incorporating adaptive control mechanisms, the CBQL-TSP method can respond to changing traffic patterns and prioritize vehicles efficiently at intersections with multiple priority types.

What potential applications or adaptations of the CBQL-TSP approach could be explored in other domains beyond transportation, such as resource allocation or decision-making in multi-agent systems

The CBQL-TSP approach can be adapted and applied in various domains beyond transportation to optimize resource allocation and decision-making in multi-agent systems. Some potential applications and adaptations include:

Supply Chain Management: Implementing the CBQL-TSP method in supply chain networks can optimize the flow of goods and resources by prioritizing critical shipments or vehicles. By applying cooperative game theory and reinforcement learning, the system can dynamically allocate resources to enhance supply chain efficiency and responsiveness.

Energy Grid Management: Utilizing the CBQL-TSP approach in energy grid management can optimize the distribution of electricity and resources across the grid. By considering different energy sources, demand patterns, and grid constraints, the system can prioritize energy distribution to ensure reliability, sustainability, and cost-effectiveness.

Healthcare Resource Allocation: Applying the CBQL-TSP method in healthcare systems can optimize resource allocation, such as hospital beds, medical supplies, and staff assignments. By considering the urgency and priority of patient needs, the system can dynamically allocate resources to improve patient care outcomes and operational efficiency.

Smart City Planning: Integrating the CBQL-TSP approach in smart city planning can optimize urban infrastructure management, traffic flow, and public services. By considering various stakeholders, such as residents, businesses, and government agencies, the system can prioritize resources and decisions to enhance overall city livability, sustainability, and resilience.