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Accuracy and time efficiency in prediction are couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, we develop a real-time traffic information prediction model on the basis of Accurate On-line Support Vector Regression (AOSVR) in this paper, and a simplified computing method of sigmoid kernel based on cloud...
During the past few years, various traffic forecasting models have been developed to monitor traffic in network. However, these strategies rest on the assumption that the pattern that has been identified will continue into the future. Such these strategies cannot be expected to give good predictions unless this assumption is valid. In this paper, we combine an ARIMA model and SVM model for obtaining...
Acquiring the real-time information about traffic flow is one of the important steps toward the realization of ITS. In this paper, we propose a real-time traffic prediction model with warm start by integrating an accurate on-line support vector regression (AOSVR) with a corrected on-line learning algorithm that is used for improving computational rate. The forecasting implementation has showed that...
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