Advances in Computational Intelligence Techniques-Based Multi-Intersection Querying Theory for Efficient QoS in the...

Area of Science:

• Computational Intelligence
• Internet of Things (IoT)
• Traffic Management Systems

Background:

• The Internet of Things (IoT) connects physical devices for data exchange, enabling intelligent environments like smart transportation.
• Computational intelligence, utilizing learning and optimization, is crucial for advancing IoT applications.
• Traditional traffic management struggles with complex, multi-intersection traffic flow.

Purpose of the Study:

• To propose a collaborative control system for multi-intersection traffic management using intelligent sensors and multi-agent reinforcement learning.
• To reduce traffic congestion and improve the quality of service in upcoming IoT applications.
• To address the limitations of conventional variable steering lane management in complex traffic scenarios.

Main Methods:

• Development of a multi-agent reinforcement learning control system.
• Integration of intelligent sensors for real-time traffic data acquisition.
• Implementation of a priority experience replay algorithm to enhance learning efficiency and convergence speed.
• Simulation and experimental investigation of the proposed system's performance.

Main Results:

• The proposed multi-intersection variable steering lane control system effectively reduces traffic congestion.
• Significant reductions in queue length and delay time were observed.
• The system demonstrated superior performance in managing waiting times compared to other control methods.
• Enhanced coordination of variable steerable lanes improved overall road network capacity.

Conclusions:

• The intelligent sensor-based, multi-intersection variable steering lane system is an effective traffic control mechanism.
• The approach significantly improves traffic flow efficiency and quality of service in complex road networks.
• This research contributes a novel solution for next-generation IoT-enabled intelligent transportation systems.