Dynamic Analysis of Kalman Filter for Traffic Flow Forecasting in Sensornets
Intelligent Transportation System (ITS) plays an important role in traffic controlling and traffic guidance which can greatly reduce the cost and pollution. In some senses, efficient traffic controlling is supported by accurate forecasting of the traffic flow and Kalman filter is one of the most used techniques. This study researches some dynamic behaviors of the Kalman filter in traffic flow forecasting. First, some observation nodes are proper set and the traffic flow data can be collected. Then Kalman filter is introduced to forecast the future traffic flow. Finally, the relation between dynamic parameters and forecasting result is analyzed. Several experiments are introduced to verify the conclusion.
Cited References Fulltext