Intelligent traffic signal synchronization using fuzzy logic and Q-learning


In the past decade, urban traffic has increased tremendously. As a result, the urban population has to invest more time in traveling. Increased road traffic results in an increased number of road accidents and more consumption of fuel, thus wasting energy. Hence for solving this issue, this paper proposes a traffic signal synchronization system which takes real time traffic signal data as input and with the implementation of multi-agent fuzzy logic, it introduces the design of an intelligent system which would smoothen the overall road traffic of the city. Fuzzy system is capable of handling the various levels of uncertainties found in the input data taken from the traffic signals. Since fuzzy logic system needs expert knowledge for its rule base and the rule base remains unchanged once defined, this paper adds up Q-learning module so that the system learns by itself by updating the set of rule base.

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@article{Iyer2016IntelligentTS, title={Intelligent traffic signal synchronization using fuzzy logic and Q-learning}, author={Vignesh Iyer and Rashmi Jadhav and Unnati Mavchi and Jibi Abraham}, journal={2016 International Conference on Computing, Analytics and Security Trends (CAST)}, year={2016}, pages={156-161} }