Dissertation > Excellent graduate degree dissertation topics show

The Research of Traffic Flow Prediction Based on Wavelets Packet Analysis and SVM

Author: MaoXiaoFei
Tutor: HuDan
School: West China University
Course: Mechanical and Electronic Engineering
Keywords: Traffic Flow Forecast (TFF) Wavelet Packet Decomposition And Reconstruction Support Vector Regression Machine (SVRM)
CLC: U491.113
Type: Master's thesis
Year: 2011
Downloads: 87
Quote: 0
Read: Download Dissertation

Abstract


According with the development of economic, expanding of cities, the developing cities are facing the problem of traffic congestion urgently. Nowadays, the problem of traffic jams and congestion is a direct acting factor for quality of life. The current traffic command scheduling method almost depends on the time, but not on actual situation of the road traffic flow status. The traffic flow is high uncertain and complicated composition. Traditional traffic flow predictive methods are used to deal with traffic data directly in time domain, whose anti-interference ability is poor and data approximation performance is bad. On the analysis of traffic flow characteristic basis, this paper considers synthetically the advantages and disadvantages of existing prediction method, so that this paper adopts wavelet packet analysis and support vector regression machine for traffic flow forecast, to enhance practicability and accuracy for the prediction of traffic flow.First of all, the original traffic flow data should be decomposed by wavelet packet analysis theory, and the original data is decomposed into four frequency band traffic flow characteristic data according to the different frequency of energy. Then, we reconstruct these four frequency characteristic data and analyze the characteristic data using ordinary decompose tree and the optimal decomposition tree to get four-dimensional traffic flow characteristic data that have the same length with original data. Then, the processed traffic data are divided into the training sample data and test data.Finally, the forecast of the reconstructed traffic flow characteristic data should be made using support vector regression machine. This paper carries out traffic flow forecast separately in normal weekday and weekend. Kernel functions, punish factor etc can be sure after training the support vector regression machine, the data of traffic flow prediction as outputs could be got after inputting the rest of the traffic flow characteristic data into the trained support vector regression machine model. Meanwhile, we test the trained support vector machine (SVM) model using test data as inputs, and do the error analysis for prediction. We realize that it is more accurate to use the prediction methods in this paper comparing with the traditional neural network and time series prediction algorithm. This paper provides a new method and new ideas for the research of traffic flow prediction.

Related Dissertations

  1. Research on Signal Analysis Methods and System for Fault Sources of Gearbox,TH132.41
  2. Complex background traffic statistical research,U491.113
  3. Layout and Planning Study on Traffic Survey Observation Station of Freeway,U491.113
  4. Road Traffic Volume Forecast Based on Wavelet and Fourier Transform,U491.113
  5. A Study on Short-term Traffic Volume Forecasting Based on Non-Parametric Regression,U491.113
  6. Study on Formation Mechanism and Forecast Methods about Highway Traffic Volume,U491.113
  7. Study on Traffic Flow Reverse of Guiyang Urban Road Intersections,U491.113
  8. Research on Expressway Data Survey Technology System,U491.113
  9. Research on Speed-Flow Relationships of the Urban Roads in Beijing,U491.113
  10. Research on the Characteristic of Volume Variation Per Hour in the Urban Central Area,U491.113
  11. Based on support vector machine flow projections and status discrimination study,U491.113
  12. Study on Key Technology Forecast about Metropolitan Beltway Highway Traffic,U491.113
  13. Generalized Regression Neural Network-based Road Traffic Travel Prediction Analysis,U491.113
  14. The Application Research of Four Stages Forecast Method Based on Neural Network in Traffic Volume Prediction,U491.113
  15. Layout and Planning Study on Traffic Survey Observation Station of Freeway in Hunan,U491.113
  16. Traffic Volume Forecast Based on Combined Models of Gray Theory and Artificial Neural Networks,U491.113
  17. Forecasts of Shugang Highway Project Traffic in Rizhao Port,U491.113
  18. The Study on Issues Regarding the Traffic Volume Forecasting of Express Highway Widening,U491.113
  19. Study on the Traffic Forecast of Beijing-Taibei Expressway Project in Langfang,U491.113
  20. Optimal Traffic Counting Locations for Origin-Destination Matrix Estimation,U491.113

CLC: > Transportation > Road transport > Technical management of traffic engineering and road transport > Traffic engineering and traffic management > Traffic Survey and Planning > Traffic survey > Traffic volume and traffic density
© 2012 www.DissertationTopic.Net  Mobile