Wavelet-based Kalman Filter for Traffic Flow Forecasting in Sensornets
With the rapid development of Intelligent Transportation Systems (ITS), traffic controlling and traffic guidance have become a hot research issue. Better traffic controlling can reduce the cost and pollution efficiently. This study researches traffic flow forecasting in Sensornets. First, some observation nodes are proper set and the traffic flow data can be collected. Then wavelet is introduced to reduce the noise of the traffic flow data. Kalman filter is also introduced to forecast the next traffic flow and the result will be more advantageous for traffic controlling. The efficiency and the accuracy of the proposed model are shown in presented numerical examples.
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