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Road Traffic Prediction Using KNN and Optimized Multilayer Perceptron

机译:使用KNN和优化多层扫描器的道路交通预测

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With the growth of population, numbers of vehicles have also grown exponentially but the traffic management system has not developed at the same pace. As a result, traffic has become a major issue in major cities and countries of the world, traffic jams have increased leading to more accidents and pollution. This paper presents a novel machine learning strategy which uses KNN and Multilayer Neural Networks for the purpose of predicting traffic in better way. Genetic Algorithm has been used to optimize the results of MLP. The proposed system can solve the traffic problem up to great extent without use of any man-power. It also highlights the better accuracy of the proposed model than using Multilayer Neural Network or any of these alone and gives the future scope of our model.
机译:随着人口的增长,车辆数量也呈指数增长,但交通管理系统并没有以同样的步伐发展。 因此,交通已成为世界各国和世界各国的主要问题,交通拥堵增加了导致更多的事故和污染。 本文介绍了一种新颖的机器学习策略,用于以更好的方式预测交通的knn和多层神经网络。 遗传算法已被用于优化MLP的结果。 建议的系统可以在很大程度上在不使用任何人力的情况下解决交通问题。 它还强调了所提出的模型的更好准确性,而不是使用多层神经网络或其中任何一个,并给出了我们模型的未来范围。

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