Traffic flow prophecy with mapreduce job for big data driven

In the current engross world, traffic overflow is a common problem for the metropolises. In spite of increasing the size of transportation systems and prompting the public transportation may increase the traffic overflow. This kind of traffic overflow problem cannot be solved manually. Today the traffic data has been entered and erupted the time of huge transportation of the data. Hence it is important to predict the traffic information on the accurate and timeline based, for forming the successful Intelligent Transportation System (ITS). In the existing system, the forecasting of traffic flow is depend on the short term of spectral analysis [3] with the small amount of data. This paper focuses on predicting the traffic flow of taxes traveling from multiple sources to the destination on basis of Nearest Neighbor (NN) approach on a Hadoop platform. The performance of the proposed algorithm is measured using metrics like MAPE, RMSE, MAE, and ME. The result shows that the proposed algorithm gives the better accuracy than the existing approach on a Hadoop platform.