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  本文關(guān)鍵詞： 多變量 混沌 相空間重構(gòu) 支持向量機 交通流預(yù)測　出處：《重慶交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：實時準確的交通流預(yù)測是交通信號控制系統(tǒng)和交通流誘導(dǎo)系統(tǒng)應(yīng)用的前提和關(guān)鍵，其預(yù)測精度直接關(guān)系到交通控制和交通誘導(dǎo)的運行效果。由于交通系統(tǒng)具有隨機性、時變性、強非線性等特點，因此人工智能方法越來越受到人們的重視。支持向量機是一種基于結(jié)構(gòu)風(fēng)險最小化和統(tǒng)計學(xué)習(xí)理論的機器學(xué)習(xí)方法，它能有效地解決小樣本、非線性、高維數(shù)以及局部極值等模式識別問題。此外，研究表明交通流還具有混沌特性。因此，將支持向量機和混沌理論結(jié)合應(yīng)用于短時交通流預(yù)測中具有重要意義。本文首先總結(jié)了國內(nèi)外短時交通流預(yù)測現(xiàn)狀；然后分析證明了交通流數(shù)據(jù)的混沌特性；最后以此為基礎(chǔ)，提出了一種基于支持向量回歸機（Support Vector Regression，SVR）和多變量相空間重構(gòu)的短時交通流預(yù)測模型。本文的創(chuàng)新點主要體現(xiàn)在模型的設(shè)計原理上，，即本文采用多變量時間序列進行建模。本文主要研究工作如下： ①在介紹PeMS12.3數(shù)據(jù)庫的基礎(chǔ)上，分析了交通流基本特征參數(shù)（交通流量、占有率和平均速度），研究了交通流數(shù)據(jù)的預(yù)處理方法，并完成了對實測交通流數(shù)據(jù)的預(yù)處理：缺失或錯誤數(shù)據(jù)的預(yù)處理、降噪處理。 ②在概述混沌理論的基礎(chǔ)上，介紹了多變量相空間重構(gòu)理論，并完成了對預(yù)處理后的交通流數(shù)據(jù)的實驗，得出了交通流量、占有率、平均速度時間序列的嵌入維數(shù)和延遲時間，以及實現(xiàn)了多變量相空間重構(gòu)。 ③在分析交通流混沌特性及其混沌特性判別方法的基礎(chǔ)上，對交通流量、占有率、平均速度時間序列進行最大Lyapunov指數(shù)的計算，結(jié)果驗證了這三種序列都具有混沌特性。 ④結(jié)合混沌理論及支持向量回歸機原理，利用遺傳算法對支持向量回歸機參數(shù)進行優(yōu)化選取，構(gòu)建了基于多變量相空間重構(gòu)的SVR短時交通流預(yù)測模型，提出了交通流預(yù)測流程，給出了預(yù)測評價指標（平均絕對誤差、平均相對誤差、均方誤差），最后利用該模型對實測交通流數(shù)據(jù)進行了實驗，同時與基于單變量相空間重構(gòu)的SVR預(yù)測模型進行了比較。 實驗結(jié)果表明：本文提出的基于多變量相空間重構(gòu)的SVR短時交通流預(yù)測模型的平均絕對誤差、平均相對誤差和均方誤差均小于基于單變量相空間重構(gòu)的SVR預(yù)測模型。說明了本文提出的模型預(yù)測效果更好，較充分地驗證了本文提出的模型能有效地進行短時交通流預(yù)測。
[Abstract]:The real-time and accurate prediction of traffic flow is the precondition and key of the application of traffic signal control system and traffic flow guidance system. Its prediction accuracy is directly related to the operation effect of traffic control and traffic guidance. Because of its randomness, time-varying, strong nonlinear and so on. Support vector machine (SVM) is a machine learning method based on structural risk minimization and statistical learning theory. The problem of pattern recognition such as high dimension and local extremum. In addition, the study shows that the traffic flow also has chaotic characteristics. It is of great significance to apply support vector machine and chaos theory to short-term traffic flow prediction. Firstly, this paper summarizes the current situation of short-term traffic flow prediction at home and abroad. Then the chaotic characteristics of traffic flow data are analyzed and proved. Finally, a support Vector Regression based on support vector regression is proposed. SVR) and multi-variable phase space reconstruction of short-term traffic flow prediction model. The innovation of this paper is mainly reflected in the design principle of the model. That is, this paper uses multivariable time series to model. The main research work of this paper is as follows: 1 based on the introduction of PeMS12.3 database, the basic characteristic parameters of traffic flow (traffic flow, occupancy rate and average speed) are analyzed, and the preprocessing method of traffic flow data is studied. The preprocessing of the measured traffic flow data is completed: the missing or wrong data preprocessing and the noise reduction. 2 on the basis of summarizing chaos theory, the theory of multi-variable phase space reconstruction is introduced, and the experiment of pre-processing traffic flow data is completed, and the traffic flow and occupation rate are obtained. The embedding dimension and delay time of average velocity time series and the reconstruction of multivariable phase space are realized. 3 on the basis of analyzing the chaos characteristic of traffic flow and its distinguishing method, the maximum Lyapunov exponent is calculated for traffic flow, occupation rate and average velocity time series. The results show that the three sequences are chaotic. 4 combined with chaos theory and support vector regression machine principle, the parameters of support vector regression machine are optimized by genetic algorithm, and the SVR short-term traffic flow prediction model based on multi-variable phase space reconstruction is constructed. The flow chart of traffic flow prediction is put forward, and the evaluation indexes (mean absolute error, mean relative error, mean square error) are given. Finally, the model is used to test the measured traffic flow data. At the same time, it is compared with the SVR prediction model based on single variable phase space reconstruction. The experimental results show that the proposed SVR short-time traffic flow prediction model based on multi-variable phase space reconstruction has an average absolute error. The average relative error and mean square error are smaller than the SVR prediction model based on single-variable phase space reconstruction. It is fully verified that the proposed model can effectively predict short-time traffic flow.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.112

【參考文獻】

相關(guān)期刊論文 前10條

1 李英,劉豹,馬壽峰;交通流時間序列中混沌特性判定的替代數(shù)據(jù)方法[J];系統(tǒng)工程;2000年06期

2 楊世堅,賀國光;基于模糊C均值聚類和神經(jīng)網(wǎng)絡(luò)的短時交通流預(yù)測方法[J];系統(tǒng)工程;2004年08期

3 王進;史其信;;短時交通流預(yù)測模型綜述[J];中國公共安全(學(xué)術(shù)卷);2005年01期

4 王曉,雋志才,樸基男,賈洪飛;局部比較的變點統(tǒng)計理論及其在交通流突變研究中的應(yīng)用[J];公路交通科技;2002年06期

5 劉靜,關(guān)偉;交通流預(yù)測方法綜述[J];公路交通科技;2004年03期

6 孫占全;潘景山;張贊軍;張立東;丁青艷;;基于主成分分析與支持向量機結(jié)合的交通流預(yù)測[J];公路交通科技;2009年05期

7 倪利華;陳笑蓉;;ARIMA模型結(jié)合小波去噪的貴陽城市交通流預(yù)測[J];貴州大學(xué)學(xué)報(自然科學(xué)版);2011年05期

8 林鑫;王曉曄;王卓;張德干;;基于蟻群聚類算法的RBF神經(jīng)網(wǎng)絡(luò)交通流預(yù)測[J];河北工業(yè)大學(xué)學(xué)報;2010年03期

9 曹成濤;徐建閩;;基于PSO-SVM的短期交通流預(yù)測方法[J];計算機工程與應(yīng)用;2007年15期

10 陸琳;張虹;;城市短時交通流預(yù)測仿真研究[J];計算機仿真;2012年05期

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