• Jappreet kaur

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 K-means clustering 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 model than using Multilayer Neural Network or any of these alone and gives the future scope of our model.

Neural Networks, K-means Clustering, Traffic-flow Prediction

"Road Traffic Prediction Using K-Means Clustering and Optimized Multilayer Perceptron Using Genetic Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.581-587, June 2019, Available :http://www.jetir.org/papers/JETIR1907F77.pdf

"Road Traffic Prediction Using K-Means Clustering and Optimized Multilayer Perceptron Using Genetic Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp581-587, June 2019, Available at : http://www.jetir.org/papers/JETIR1907F77.pdf