Fuzzy kalman filtering-based traffic flow parameter prediction method

Abstract

The invention discloses a fuzzy kalman filtering-based traffic flow parameter prediction method, which is characterized by comprising the following steps of: laying a detector in each lane on the upstream of a road section and in each lane on the downstream of the road section to acquire real-time traffic flow parameter data; acquiring contemporaneous historical traffic flow parameter data of the detector in each lane; constructing a dynamic kalman filtering traffic flow parameter prediction model by using a kalman filtering technology; obtaining a kalman filtering parameter prediction result through a kalman filtering time updating equation and a state updating equation; introducing a historical contemporaneous average traffic flow parameter into a formula (2) and constructing a fuzzy kalman filtering traffic flow parameter prediction model; and predicting the traffic flow parameter of a next time interval and behind the next time interval according to the real-time traffic flow parameter acquired by the detector and the historical contemporaneous average traffic flow parameter.

Description

technical field [0001] The invention relates to a traffic flow prediction method. Background technique [0002] The short-term traffic flow parameter prediction is based on the real-time traffic data obtained by the detection equipment, and predicts the traffic flow parameters in the next period through the constructed model and method. The prediction results are usually used in real-time traffic signal control systems and dynamic guidance systems. Therefore, the prediction of short-term traffic flow parameters will directly affect the real-time effect of the above-mentioned system. [0003] From the perspective of forecasting models, short-term traffic flow forecasting can be divided into two categories: the first category is based on traditional mathematical and physical methods such as mathematical statistics and calculus, mainly including time series models, Kalman filter models, parameter Regression model, exponential smoothing model, etc.; the second type is a predic...

AI Technical Summary

Problems solved by technology

However, the existing Kalman filter prediction model predicts the output value to be delayed for several periods of time, that is, there is a certain hysteresis, which affects the wide application of prediction parameters in dynamic real-time control and in induction systems.

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