Effective traffic light timing, particularly optimal light cycle programs, is critical in today's cities. Considering possible savings in energy use and traffic, pedestrian safety, traffic flow management, and environmental concerns are all challenges that need to be addressed. Artificial intelligence can be used to solve this challenge. We have proposed the use of an optimization-based approach, particle swarm optimization (PSO), to identify road traffic signal cycle programs in this research work. Results obtained are recreated using a recognized urban mobility simulator of microscopic traffic. We performed tests for this study. Ranchi, Jharkhand (India) traffic lights demonstrated that our system could provide effective traffic light cycle program. The optimization technique PSO used over here achieved measurable gains for the two key objectives: (1) the number of vehicles that arrive at their location and (2) the overall journey duration compared to expertly established cycle program (which were similar to real ones). In this study, we have observed an improvement to achieve high performance to determine the traffic light cycle program. The obtained results shows that around 24% of improvement can be achieved using the proposed technique.


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    Title :

    PSO-Based Traffic Signals in a Real-World City


    Additional title:

    Lect. Notes Electrical Eng.



    Conference:

    International Conference on Advances in Data Science and Computing Technologies ; 2022 ; Kolkata, India June 23, 2022 - June 24, 2022



    Publication date :

    2023-09-30


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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