A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Research on traffic signal control based on intelligence techniques
This paper studies traffic signal control method based on intelligent techniques such as agent, fuzzy logic system (FLS), neural network-fuzzy (NNF) and multi-objective genetic algorithms (MOGA) for intersection. The traffic signal control system of intersections in local area can be built up by using the term of agent, and it comprises four levels: centre command layer, local area coordination layer, isolated intersection control layer, and optimizing layer. This paper focus on discussing isolated intersection control layer and optimizing layer. In an isolated intersection layer, fuzzy logic system is used to control traffic signal, and input parameters of fuzzy system can be forecasted or calculated by neural network-fuzzy. In optimizing layer, parameters in fuzzy system can be optimized by MOGA. The proposed method has the adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes for intersections in local area. Our proposed has the ability to adjust its signal timing in response to changing traffic conditions on a real-time basis.
Research on traffic signal control based on intelligence techniques
This paper studies traffic signal control method based on intelligent techniques such as agent, fuzzy logic system (FLS), neural network-fuzzy (NNF) and multi-objective genetic algorithms (MOGA) for intersection. The traffic signal control system of intersections in local area can be built up by using the term of agent, and it comprises four levels: centre command layer, local area coordination layer, isolated intersection control layer, and optimizing layer. This paper focus on discussing isolated intersection control layer and optimizing layer. In an isolated intersection layer, fuzzy logic system is used to control traffic signal, and input parameters of fuzzy system can be forecasted or calculated by neural network-fuzzy. In optimizing layer, parameters in fuzzy system can be optimized by MOGA. The proposed method has the adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes for intersections in local area. Our proposed has the ability to adjust its signal timing in response to changing traffic conditions on a real-time basis.
Research on traffic signal control based on intelligence techniques
Wei Wu, (author) / Wang Mingjun, (author)
2003-01-01
351302 byte
Conference paper
Electronic Resource
English
Research on Traffic Signal Control Based on Intelligence Techniques
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