Adaptive Traffic Signal Control Using Distributed Algorithms
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Solution Overview
Problem
Existing adaptive traffic control systems are not scalable and difficult to optimize, failing to effectively utilize real-time traffic data to improve congestion and resiliency in traffic incidents.
Innovation Solution
A traffic signal controller and method that determine offset and green time split values using processors and distributed algorithms based on real-time traffic flow performance metrics, allowing for dynamic adjustment of traffic signal control to optimize traffic flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing adaptive traffic control systems are used, then traffic flow performance is improved, but scalability and optimization difficulty worsen
Solution Approach 1:
The system divides the traffic network into multiple zones with dedicated controllers that independently manage local intersections. Each zone controller operates autonomously based on local traffic conditions, enabling modular expansion without requiring system-wide reconfiguration. This segmentation allows the system to scale from single intersections to entire arterial networks while maintaining optimization performance.
Solution Approach 2:
The system implements dynamic adjustment of green time splits and offset values in real-time based on current traffic flow conditions. Controllers continuously monitor traffic metrics and automatically modify signal timing parameters, enabling the system to adapt to changing conditions without manual reconfiguration. This dynamic operation allows the same infrastructure to optimize performance across diverse traffic scenarios.
2Productivity
If real-time traffic data is utilized for control, then congestion reduction is improved, but control system complexity increases
Solution Approach 1:
The control system automatically processes real-time traffic data and adjusts signal timing without requiring manual intervention. Controllers self-calibrate using local traffic metrics, automatically determining optimal green time splits and offsets. This self-service capability reduces the need for complex manual optimization procedures while maintaining effective congestion management based on real-time conditions.
Solution Approach 2:
The system optimizes traffic flow by dynamically changing key control parameters including green time split values and offset times. Controllers adjust these parameters in response to real-time traffic metrics, transforming fixed timing schedules into adaptive control strategies. This parameter optimization approach enables congestion reduction through automated real-time adjustments rather than complex manual control systems.
Data Source
AI summary
The disclosure presents methods and apparatus for adaptive control of one or more traffic signals. A method may include determining an offset value based on a function of a traffic flow performance metric. The method may further include determining a green time split value based on a distributed algorithm. The method may further include adaptive control of the one or more traffic signals based on the green time split value and the offset value.