Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

Area of Science:

• Intelligent Transportation Systems
• Urban Planning
• Environmental Engineering

Background:

• Urban traffic networks face challenges in performance improvement due to the need for cooperation among diverse entities like vehicles and infrastructure.
• Smart city initiatives leverage sensor networks to gather information for managing urban complexities.
• Air pollution in cities stems from various sources, including but not limited to road traffic.

Purpose of the Study:

• To propose a cooperative control algorithm for urban subsystems to address urban mobility challenges.
• To develop an interconnected traffic lights controller (TLC) network that adapts signal cycles using traffic and air pollution data.
• To improve the overall performance of urban traffic networks.

Main Methods:

• A system of systems engineering approach was applied due to the distributed and heterogeneous nature of system components.
• A consensus-based control algorithm was designed, incorporating a component for reaching consensus on the state of TLC network components.
• Discrete event systems specification was utilized for modeling and simulation.

Main Results:

• Simulation studies demonstrated promising results for the proposed cooperative control strategy.
• The algorithm effectively handles simultaneous responses to both pollution levels and traffic flow dynamics.
• The interconnected TLC network successfully adapted traffic light cycles based on sensory information.

Conclusions:

• The developed consensus-based cooperative control algorithm offers a viable solution for enhancing urban traffic network performance.
• The approach effectively integrates traffic management and environmental monitoring for smarter urban mobility.
• The system shows significant potential for real-world application in smart cities aiming to reduce pollution and congestion.