TRAFFIC ANALYSIS AND PREDICTION SYSTEM BY THE USE OF MODIFIED ARIMA MODEL
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Abstract
Traffic Prediction is critical as it is enhancing day by day leading to worst on road situations. Increased accidents and delays in critical applications is causing awful situations for the user. In order to resolve the problem Modified ARIMA is used. Modified ARIMA is implied over the dataset. The dataset for the implication is fetched from online source. The UCI website is used for traffic dataset time series analysis. Modified ARIMA is used to make stationery time series from dynamic series at AR phase. MI phase is used to predict number of previous values to be analyzed and MA phase is hybridized using KNN with Euclidean distance. The result of the proposed literature is presented in terms of accuracy and mean square error. Result shows significant improvement in terms of accuracy and means square error.
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