基于SVR的數(shù)據(jù)預處理分析與研究
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本文關鍵詞:基于SVR的數(shù)據(jù)預處理分析與研究 出處:《蘭州理工大學》2014年碩士論文 論文類型:學位論文
更多相關文章: 交通流預測 支持向量回歸機 數(shù)據(jù)預處理 時空相關性 相鄰路段
【摘要】:隨著我國經濟的增長和城市化進展,交通擁堵、交通事故頻發(fā)、尾氣污染等交通問題已經成為當今社會普遍關注的焦點。實時而準確的短時交通流量預測可以為城市交通誘導和控制提供數(shù)據(jù)支持,是解決多種交通問題的關鍵和基礎。本論文針對斷面交通流預測數(shù)據(jù)中往往存在的錯誤、缺失、包含較多噪聲等問題,結合SVR預測模型,提出了一種新的數(shù)據(jù)預處理方法,根據(jù)路網交通流信息中隱含的時空關系,增加了對目標路段上游交通流數(shù)據(jù)在時間、空間上的相關性分析和處理,其優(yōu)點在于降低了預測過程中的不確定性,適應了交通流的隨機變化,并結合支持向量回歸機所具備的推廣能力和對小樣本數(shù)據(jù)具有的較強適應性,提高了預測的精度與泛化能力。最后本論文結合常用的數(shù)據(jù)預處理技術,對比未使用本論文預處理的SVR模型和神經網絡模型,驗證了使用本論文方法的模型擬合度有明顯的提高,均方誤差也明顯減小,并且得出了最優(yōu)的預測方案。 通過對目標路段上游的交通流數(shù)據(jù)進行時空相關性分析,選取對目標路段交通流預測影響較大的路段和其相關數(shù)據(jù),并基于線性回歸的思想構建模型,將該模型計算的結果運用到SVR模型的數(shù)據(jù)集中,在避免了數(shù)據(jù)丟失的同時,既有效的壓縮了數(shù)據(jù)集特征數(shù),降低了計算量,也提高了在預測模型的預測精度和泛化能力。實驗結合ε-SVR模型,驗證了本論文預處理方法的有效性,并且顯著提高了原模型的預測精度,減少了預處理模型的待估參數(shù),提高了模型的計算效率。
[Abstract]:With the economic growth and urbanization in China, traffic congestion and traffic accidents occur frequently. Emission pollution and other traffic problems have become the focus of attention in the society. Real-time and accurate short-term traffic flow prediction can provide data support for urban traffic guidance and control. It is the key and foundation to solve a variety of traffic problems. This paper combines the SVR forecasting model to solve the problems of error, lack and noise in the cross-section traffic flow prediction data. A new data preprocessing method is proposed. According to the spatial and temporal relationship implied in the traffic flow information of the road network, the correlation analysis and processing of the upstream traffic flow data of the target section in time and space are added. It has the advantages of reducing the uncertainty in the prediction process, adapting to the random change of traffic flow, and combining the generalization ability of support vector regression machine and the strong adaptability to small sample data. The prediction accuracy and generalization ability are improved. Finally, the SVR model and the neural network model which are not preprocessed in this paper are compared with common data preprocessing techniques in this paper. It is verified that the fitting degree of the model using the method in this paper is obviously improved and the mean square error is obviously reduced, and the optimal prediction scheme is obtained. Through the spatio-temporal correlation analysis of the upstream traffic flow data of the target section, the section and its related data which have a great influence on the traffic flow prediction of the target road section are selected, and the model is constructed based on the idea of linear regression. The result of this model is applied to the data set of SVR model, which can not only avoid the data loss, but also effectively compress the feature number of the data set and reduce the calculation amount. It also improves the prediction accuracy and generalization ability of the prediction model. Experiments combined with 蔚 -SVR model verify the effectiveness of the preprocessing method and improve the prediction accuracy of the original model significantly. The estimated parameters of the preprocessing model are reduced, and the calculation efficiency of the model is improved.
【學位授予單位】:蘭州理工大學
【學位級別】:碩士
【學位授予年份】:2014
【分類號】:U495;TP18
【參考文獻】
相關博士學位論文 前2條
1 劉夢涵;面向特大城市的分層次交通擁堵評價模型及算法[D];北京交通大學;2009年
2 孫曉亮;城市道路交通狀態(tài)評價和預測方法及應用研究[D];北京交通大學;2013年
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