Prediction Of The Short Traffic Flow And Visuliazation Based Matlab

The prediction of short time traffic flow is the key of the Traffic Guidance System(TGS),and it's an important part of the Intelligent Transportation System(ITS).There have been many research methods for short time traffic flow prediction. The paper studies the theory of wavelet transformation and algorithms of decompose and reconstruction with Mallat, and the theory of Gray-Markove forecasting model and the methods of prediction in detail. Then we put forward the Gray-Markove forecasting Model based on wavelet transformation with Daubechies 5 in Matlab. The basic thought of this paper is followed:Firstly, with the wavelet toolbox in the Matlab we decompose the original traffic flow data in 5 levels with the db5 and we can get the 5 detail parts and 1 aprroximation part. Then we reconstruct the approximation part to the original level, and so do the detail parts.Secondly, we make the Gray-markov forecasting model on the reconstructed aprroximation part .This paper use the GM(1,1),which is a single factor model to simulate the history data and predict the traffic flow data of the next signal period. On the basis of the fitting data ,we can acquire the errors .And now according to the fitting errors we partition to five states and work out the transition probability of several shifts from one to five. According to the the transition probability and the times that every state appears, we can verify whether or not traffic flow data can satisfy the exclusive characteristic of the Markov Model that the prediction is independent of the history. Experiment prove that the traffic flow data fill this exclusive characteristic. .In this paper ,we apply the Markov's chain Model with weight to forecast the traffic flow. According to the transition probability matrix, the autocorrelation coefficients can be calculated, and then we can get the weights of every rank .On the basis of the results above, we can calculate the transition probability with weight. The maximum in the transition probability matrix is the forecasting state of the error of the next signal period. Then we can work out the final predicted traffic flow data.In this paper the experimental data comes from Taian of Shandong province, including the data acquired in the locale and the traffic monitoring video. From all the data we pick up 200 traffic flow data of continuous signal periods at the Post port of Dongyue Street to test the combined model. The experiment prove that the Gray-Markov forecasting Model based on the wavelet transformation can improve the precision of the prediction, and the precision is better than the precision of the forecasting result which is worked out with all kinds of models directly. Further more the Markov's chain with weight model can make up the defects of the GM(1,1) and it can correct the forecasting errors. Consequently the forecasting precision is improved greatly.