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A Survey of Intelligence Methods in Urban Traffic Signal Control


Zhiyong Liu


Vol. 7  No. 7  pp. 105-112


It is quit difficult to improve the performance of urban traffic signal control system efficiently by using traditional methods of modeling and control because of time-variability, non-linearity, fuzzyness and nondeterminacy in the system. It becomes the research hotspot in this area to apply artificial intelligence methods to urban traffic signal control system. This paper, based on analysis for a general model of urban traffic signal control, makes a summary of the applications of intelligence methods such as fuzzy logic, neural networks, evolutionary algorithms and agent reinforcement learning to urban traffic signal control, and analyzes the superiority and inferiority of these methods in applications. Finally, some points of view about the future research in this area are proposed.


Intelligent Transportation Systems, Urban Traffic Signal Control, Fuzzy Logic, Neural Networks, Evolutionary Algorithms, Reinforcement Learning, Pattern Discovery