Traffic Light Algorithm Dynamic Green Time Redistribution
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Solution Overview
Problem
Existing traffic light control algorithms fail to dynamically adjust green light durations without disrupting traffic flow, leading to congestion, as they require additional sensors and cannot increase green light time beyond predefined limits, necessitating a solution that can be implemented in existing infrastructure with a single sensor per lane and maintains the traffic cycle.
Innovation Solution
An algorithm that measures traffic usage percentage across lanes, adjusts green light durations by increasing the maximum usage lane's time and decreasing the minimum usage lane's time, while maintaining a constant total green light time, ensuring seamless traffic flow without altering the traffic cycle or infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing traffic light control algorithms dynamically adjust green light durations based on static traffic measurements, then traffic flow optimization is achieved, but the traffic cycle is disrupted and congestion arises
Solution Approach 1:
The system dynamically adjusts green light durations based on real-time traffic conditions while maintaining the overall traffic cycle structure. The controller modifies individual lane green times within the constraints of the predefined cycle, allowing adaptive optimization without disrupting the fundamental timing rhythm that prevents congestion.
Solution Approach 2:
The invention applies different green light durations to different lanes based on their specific traffic conditions. Each lane receives customized timing within the overall cycle, allowing high-traffic lanes to receive extended green time while low-traffic lanes receive reduced time, all within the framework of the stable master cycle.
2Measurement precision
If additional sensors are deployed to measure traffic flow accurately, then traffic measurement precision is improved, but deployment cost and operational cost significantly increase
Solution Approach 1:
The system uses the existing traffic sensor data in a self-service manner, processing the raw sensor outputs to derive traffic flow information without requiring additional specialized measurement devices. The controller algorithms analyze the sensor signals to extract timing and flow characteristics needed for adaptive control.
Solution Approach 2:
The existing traffic sensors, originally designed for basic detection, are made multi-functional by using their data for both detection and flow measurement. The system extracts multiple parameters (traffic volume, speed, density) from the sensor signals, making the single sensor serve multiple control functions without additional hardware.
3Loss of time
If green light time is reduced for congested lanes, then waiting time is reduced, but traffic flow capacity is decreased and congestion worsens
Solution Approach 1:
The system dynamically reallocates green light time within the traffic cycle based on real-time congestion conditions. When a lane is congested, the controller extends its green time within the cycle framework, allowing the system to adapt timing to current conditions rather than using fixed predetermined times.
Solution Approach 2:
The controller continuously monitors traffic conditions and uses this feedback to adjust green light durations. When congestion is detected in a particular lane, the system responds by extending green time for that lane, creating a closed-loop control system that adapts to changing traffic conditions and prevents further congestion buildup.
Data Source
AI summary
Described is an algorithm to optimize traffic light activity and minimize traffic congestion. Traffic conditions are monitored by sensors and the algorithm dynamically controls the green light time to account for traffic conditions and enhance the traffic flow. In one example, the green light time of each lane is reduced or increased according to traffic flow in the lane.