Dynamic traffic prediction based on traffic flow mining

ITS technology collects a large of historical traffic flow data that may provide information for the support and improvement of traffic control. Data mining technique is appropriate to analysis the large amount of ITS data to acquire useful traffic pattern. We present a dynamic traffic prediction model, the model deals with traffic flow data to convert them into traffic status. In this paper two data mining techniques, the clustering analysis and the classification analysis, are used to develop the model, and the classification model can be used to predict traffic status in real time. The experiment shows the prediction model can be used efficiently in the dynamic traffic prediction for the urban traffic flow guidance.