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Traffic Prediction on Large Scale Traffic Networks Using ARIMA and K-Means

In this paper, a method is developed to make weekly predictions for a large-scale traffic network. In order to make predictions, the time series forecasting method ARIMA is used, and the road segments of the traffic network are grouped with the K-Means Clustering algorithm based on their traffic data to speed up the prediction process. A prediction model is generated for the most representative segment of each cluster. These representative models are used to perform forecasts for each segment in the network. The developed method is applied to a large traffic network consisting of 21,843 road segments that is located at the southern part of Ankara city. The results indicate high accuracy of the models.