Study On Traffic Forecasting Method In The Urban Freeway Based Upon The Real-time Traffic Data

The short-time traffic forecasting is a significant problem in the field of traffic controlling, vehicle guidance and so on. It is important for us to study the method and theory on forecasting the traffic condition in the future 15 minutes or more. Well traffic prediction is valuable for alleviating traffic jam in the city and avoiding the social resource wasting.The freeway in is the artery in the urban road network, which take on most of the long-distance transportation. To a large extant the traffic condition in the freeway influences the quality of the trip in the city. On the back ground of the ITS (Intelligent Transportation System) how to make full use of the traffic data collected from the RTMS to forecast the traffic condition in the urban road especially the urban freeway, is meaningful and valuable to improve the efficiency of transportation, in particular for the logistics companies.The time-series method is a best way in the field of prediction. Under the condition of continuous data the time-series method can attain higher precision. Based on the continuous traffic data collected in the expressway of Beijing city, this thesis studies the traffic prediction method in the future 24 hours with the time-series method, contemporary signal filtering theory and data clustering theory. Concretely this studying includes such works as following:Applying the method of traffic threshold and the principle of traffic flow to eliminate the wild point in the traffic data and estimating the missing data with numeral analysis.Using the wavelet transform to denoise the data series.Employing data clustering theory to analyze the traffic flow velocity data in every day, and summarizing the regularity about the traffic speed in different days.Applying the time-series model-ARIMA this thesis forecasts the traffic speed in the future 24 hours, and analyzes the error of the result.Based on the theory of clustering, this thesis puts forward the method how to choose the proper history data to model, and analyzes the superiority of this methodover the traditional method choosing all history data.