Traffic Control System Using Genetic Algorithms for Network Optimization
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Solution Overview
Problem
Current traffic control systems in large cities are limited in optimizing traffic flow across an entire road network, as they lack connectivity and rely on estimated traffic conditions, leading to inefficiencies and increased congestion, especially at high loads.
Innovation Solution
A traffic control system that uses evolutionary algorithms and genetic algorithms to optimize signal plans for all traffic signal systems in a network, updating control parameters in real-time based on current traffic data to minimize waiting times, fuel consumption, and congestion, while ensuring compliance with hard and soft boundary conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traffic control systems operate autonomously at individual intersections with local optimization, then implementation complexity and cost are reduced, but network-wide traffic flow optimization is limited
Solution Approach 1:
The patent divides the road network into multiple zones, each managed by an independent traffic control unit that optimizes signal plans for its specific zone. This segmentation allows distributed optimization while maintaining manageable system complexity at each node.
Solution Approach 2:
The patent combines local autonomous control capabilities with centralized coordination mechanisms. Traffic control units exchange information and coordinate signal plans across zone boundaries, merging individual optimizations into a cohesive network-wide optimization strategy.
2Device complexity
If traffic control systems use estimated traffic conditions without real-time network connectivity, then communication infrastructure requirements are reduced, but optimization accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where traffic control units continuously receive traffic flow data from detectors, evaluate the effectiveness of current signal plans, and adjust parameters accordingly. This closed-loop control improves accuracy without requiring complex real-time communication infrastructure.
Solution Approach 2:
The patent uses historical traffic data and predictive models to pre-calculate optimal signal plans before implementing them. This preliminary action allows the system to prepare optimized control strategies in advance, improving response accuracy without requiring instantaneous communication across the entire network.
3Ease of manufacture
If fixed-time control is used instead of traffic-dependent control at high loads, then implementation cost is reduced, but traffic flow efficiency deteriorates
Solution Approach 1:
The patent implements dynamic signal plan adjustment where control parameters are automatically modified based on real-time traffic load conditions. The system transitions between different control strategies (traffic-dependent vs. fixed-time) depending on the current operational context, maintaining efficiency while adapting to varying demand levels.
Solution Approach 2:
The patent changes key control parameters such as signal cycle length, green phase duration, and offset times based on measured traffic flows. By dynamically adjusting these parameters, the system maintains optimal performance across different traffic conditions without requiring complete infrastructure changes.
4Productivity
If signal plans are updated frequently across the entire network, then traffic flow optimization is improved, but computational load and update complexity increase
Solution Approach 1:
The patent divides the network into zones with independent signal plan optimization. Each zone can be updated separately based on local traffic conditions, reducing the overall computational burden and simplifying the update process compared to network-wide simultaneous updates.
Solution Approach 2:
The patent implements partial updates where only specific signal plans or specific parameters within signal plans are updated based on current traffic conditions. This selective updating approach reduces computational load while maintaining optimization benefits in critical areas.
Data Source
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AI summary
The invention relates to a traffic control system for influencing a traffic flow in a limited road or traffic network comprising junctions, e.g. of a big city, light signal installations (LSA) corresponding to a number of junctions N and having individual controls, the signal plans currently determining the way in which said individual controls are controlled being forwarded to a traffic computer. Detectors are used in conjunction with the light signal installations (LSA) and acquire traffic data and forward them to the traffic computer. The traffic computer is adapted to operate, owing to the implementation of evolutionary algorithms or genetic algorithms including an associated termination criterion (termination) in such a manner that updated signal plans can be supplied to all N light signal installations (LSA) within a suitable time frame to optimize the traffic flow owing to the traffic flow of the road network determined by the detected traffic data. A repair mechanism is utilized when relative coding is used, or sequential coding is used to obtain the suitable time frame within which the phase transitions, which are part of a signal plan, may take place. The invention further relates to a method for operating the traffic control to influence the traffic flow and to a computer program product.