發(fā)布時(shí)間：2018-01-11 21:23

  本文關(guān)鍵詞：基于SVR的數(shù)據(jù)預(yù)處理分析與研究　出處：《蘭州理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 交通流預(yù)測(cè) 支持向量回歸機(jī) 數(shù)據(jù)預(yù)處理 時(shí)空相關(guān)性 相鄰路段

【摘要】：隨著我國經(jīng)濟(jì)的增長和城市化進(jìn)展,交通擁堵、交通事故頻發(fā)、尾氣污染等交通問題已經(jīng)成為當(dāng)今社會(huì)普遍關(guān)注的焦點(diǎn)。實(shí)時(shí)而準(zhǔn)確的短時(shí)交通流量預(yù)測(cè)可以為城市交通誘導(dǎo)和控制提供數(shù)據(jù)支持,是解決多種交通問題的關(guān)鍵和基礎(chǔ)。本論文針對(duì)斷面交通流預(yù)測(cè)數(shù)據(jù)中往往存在的錯(cuò)誤、缺失、包含較多噪聲等問題,結(jié)合SVR預(yù)測(cè)模型,提出了一種新的數(shù)據(jù)預(yù)處理方法,根據(jù)路網(wǎng)交通流信息中隱含的時(shí)空關(guān)系,增加了對(duì)目標(biāo)路段上游交通流數(shù)據(jù)在時(shí)間、空間上的相關(guān)性分析和處理,其優(yōu)點(diǎn)在于降低了預(yù)測(cè)過程中的不確定性,適應(yīng)了交通流的隨機(jī)變化,并結(jié)合支持向量回歸機(jī)所具備的推廣能力和對(duì)小樣本數(shù)據(jù)具有的較強(qiáng)適應(yīng)性,提高了預(yù)測(cè)的精度與泛化能力。最后本論文結(jié)合常用的數(shù)據(jù)預(yù)處理技術(shù),對(duì)比未使用本論文預(yù)處理的SVR模型和神經(jīng)網(wǎng)絡(luò)模型,驗(yàn)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(yù)測(cè)方案。 通過對(duì)目標(biāo)路段上游的交通流數(shù)據(jù)進(jìn)行時(shí)空相關(guān)性分析,選取對(duì)目標(biāo)路段交通流預(yù)測(cè)影響較大的路段和其相關(guān)數(shù)據(jù),并基于線性回歸的思想構(gòu)建模型,將該模型計(jì)算的結(jié)果運(yùn)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(shí),既有效的壓縮了數(shù)據(jù)集特征數(shù),降低了計(jì)算量,也提高了在預(yù)測(cè)模型的預(yù)測(cè)精度和泛化能力。實(shí)驗(yàn)結(jié)合ε-SVR模型,驗(yàn)證了本論文預(yù)處理方法的有效性,并且顯著提高了原模型的預(yù)測(cè)精度,減少了預(yù)處理模型的待估參數(shù),提高了模型的計(jì)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(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;TP18

【參考文獻(xiàn)】

相關(guān)博士學(xué)位論文 前2條

1 劉夢(mèng)涵;面向特大城市的分層次交通擁堵評(píng)價(jià)模型及算法[D];北京交通大學(xué);2009年

2 孫曉亮;城市道路交通狀態(tài)評(píng)價(jià)和預(yù)測(cè)方法及應(yīng)用研究[D];北京交通大學(xué);2013年

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本文編號(hào)：1411252