Optimization of traffic light has been a hotspot for years, because of technology advancement for the latest 20 years, high demand in international market for car companies to develop, produce more cars and also geographic conditions, high standardatization of live enhance this problem. In this paper I will use an approach in traffic light optimization by using machine learrning technique, to train a set of data, in order to compute and produce best solutions for optimization of traffic light. There have been many methods used such Webster method, Pedri Net algorithm model, fuzzy model and so on. I will use a different approach to fuzzy method, with intention to provide better output result, decrease the amount of released gases in atmosphere, lower delay and waiting time of cars in a traffic jam. In metropol cities consisting million of people where urban infrastructure is complex, only the development and improvement of these methods can make people’s live more simple. One of most early algorithms to minimize cost in travelling through one or lots of routes is Djikstras algorithm, where simultaneous tests to use different routes, chose the best route, thus minimizing consumption and increasing output efficiency. The paper will be divided into several sections: Introduction, where a set of definitions, general terms of traffic light software simulation are presented, Big Data, representing the dataset of input where in Artificial Intelligence are the building ‘bricks’ into comparing and anlazying the ouptut results of different methods/algorithms (Pedri Net algorithm, fuzzy model, improved RNN Djikstra Algorithm etc.). The upcoming section Methodology, gives a general idea of into analyzing Webster’s algorithm and breaking it down into smaller parts, The Derivation of Fuzzy Method, when analyzing bits of components, methodology used in traffic light system and proposed smart system nowadays, fuzzy method is one of the roots in considering clustering techniques, VANet System Architecture, in this section a proposed system architecture is used, it is one of the most importants sections because it approaches a solution, which is a derived form of Internet of Thing (IoT) components into achieving a Smart City System, Result section gives output where in the upcoming sections can be used as proposals and ideas. Neural Network, CNN (Convolution Neural Network), DL (Deep Learning) and RNN (Recurrent Neural Network) are sections dedicated to relation between artificial intelligense and smart city implementation, where the main idea is training large amounts of datasets to propose smart, efficient and reliable solutions. In conlusion and future work section, the proposed solutions underline the importance of correlation between general methods: Fuzzy, Webster method with big datasets (machine learning techniques) and future work ideas highlight the necessity of virtualization and doubling – quadrupling the layers of CNN, which is proportional to hardware computational cost.
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Dec 9, 2023
Zakiyah Zakiyah + 2
Open Access