發(fā)布時(shí)間：2019-06-13 17:38

【摘要】：隨著城市化的不斷發(fā)展,城市中交通流量的快速增長(zhǎng)與道路基礎(chǔ)設(shè)施增長(zhǎng)緩慢之間的矛盾越來越凸顯。尤其是特大城市的擁堵現(xiàn)象越來越嚴(yán)重,這已經(jīng)嚴(yán)重制約我國(guó)的城市交通可持續(xù)發(fā)展戰(zhàn)略的繼續(xù)推進(jìn)。智能城市交通系統(tǒng)(ITS)被看作是緩解這些問題的有效方法之一。因此,本文以多斷面相似性為基礎(chǔ),討論了交通流區(qū)間預(yù)測(cè)方法。本文研究的思路是基于已經(jīng)獲取的歷史數(shù)據(jù),以現(xiàn)有的交通流預(yù)測(cè)技術(shù)為藍(lán)本,對(duì)交通流數(shù)據(jù)變化規(guī)律進(jìn)行分析,研究其發(fā)展趨勢(shì)并進(jìn)行歸納,以達(dá)到對(duì)未來交通流的變化趨向進(jìn)行準(zhǔn)確預(yù)測(cè)的目的。本文主要研究工作如下:(1)針對(duì)交通流數(shù)據(jù)的內(nèi)在相關(guān)性進(jìn)行了分析,主要以時(shí)間序列為工具討論并處理交通流數(shù)據(jù)的相關(guān)性及其特征。(2)針對(duì)交通流數(shù)據(jù)的斷面相關(guān)性進(jìn)行了分析,把兩個(gè)或者多個(gè)相鄰點(diǎn)看作一個(gè)整體,分析其臨近點(diǎn)交通流的變化趨勢(shì)以及引起變化的多種因素,提出了基于多斷面相關(guān)性的預(yù)測(cè)算法,并給了相應(yīng)的分析方法。(3)結(jié)合以上基于時(shí)間與空間相關(guān)性分析方法,改進(jìn)了常規(guī)交通流預(yù)測(cè)算法,對(duì)其點(diǎn)預(yù)測(cè)結(jié)果進(jìn)行置信區(qū)間計(jì)算,從而得到預(yù)測(cè)區(qū)間,提出了基于多斷面相關(guān)性的交通流區(qū)間預(yù)測(cè)算法,該預(yù)測(cè)方法的模型主體是支持向量機(jī)(SVM)回歸模型。(4)針對(duì)以上預(yù)測(cè)方法做了進(jìn)一步的改進(jìn)與增強(qiáng),引入了Boosting增強(qiáng)算法。該算法通過使用重采樣技術(shù)來進(jìn)行自動(dòng)的權(quán)值重置和組合,經(jīng)過多次選擇并訓(xùn)練數(shù)據(jù),希望能夠提高分類器性能。其核心思想是在訓(xùn)練新的分類器時(shí),著重訓(xùn)練那些相對(duì)更難被正確分類的樣本。
[Abstract]:With the continuous development of urbanization, the contradiction between the rapid growth of traffic flow and the slow growth of road infrastructure is becoming more and more prominent. Especially, the congestion phenomenon of mega-cities is becoming more and more serious, which has seriously restricted the sustainable development strategy of urban traffic in our country. Intelligent urban traffic system (ITS) is regarded as one of the effective methods to alleviate these problems. Therefore, based on the multi-section similarity, the traffic flow interval prediction method is discussed in this paper. The idea of this paper is based on the obtained historical data, based on the existing traffic flow prediction technology, the change law of traffic flow data is analyzed, its development trend is studied and summarized, in order to achieve the purpose of accurate prediction of the change trend of traffic flow in the future. The main research work of this paper is as follows: (1) the internal correlation of traffic flow data is analyzed, and the correlation and characteristics of traffic flow data are discussed and processed with time series as a tool. (2) the cross-section correlation of traffic flow data is analyzed, two or more adjacent points are regarded as a whole, and the changing trend of traffic flow and many factors causing the change are analyzed. A prediction algorithm based on multi-section correlation is proposed, and the corresponding analysis method is given. (3) combined with the above analysis methods based on time and space correlation, the conventional traffic flow prediction algorithm is improved, and the confidence interval of the point prediction results is calculated, thus the prediction interval is obtained, and the traffic flow interval prediction algorithm based on multi-section correlation is proposed. The main body of the prediction method is the support vector machine (SVM) regression model. (4) the above prediction method is further improved and enhanced, and the Boosting enhancement algorithm is introduced. By using resampling technology to reset and combine the weights automatically, the algorithm hopes to improve the performance of the classifiers by selecting and training the data many times. The core idea is to train the samples which are relatively difficult to be classified correctly when training the new classifiers.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.14

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉齊林;曾玲;曾祥艷;;基于支持向量機(jī)的區(qū)間模糊數(shù)時(shí)間序列預(yù)測(cè)[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2015年22期

2 劉巖;張寧;邵星杰;;城市軌道交通斷面客流短時(shí)預(yù)測(cè)[J];都市快軌交通;2015年01期

3 周桐;楊智勇;孫棣華;魏方強(qiáng);;分車型的高速公路短時(shí)交通流量預(yù)測(cè)方法研究[J];計(jì)算機(jī)應(yīng)用研究;2015年07期

4 王惟;;一種基于粒子群優(yōu)化SVM的交通流量預(yù)測(cè)方法[J];運(yùn)城學(xué)院學(xué)報(bào);2014年02期

5 朱征宇;劉琳;崔明;;一種結(jié)合SVM與卡爾曼濾波的短時(shí)交通流預(yù)測(cè)模型[J];計(jì)算機(jī)科學(xué);2013年10期

6 袁健;范炳全;;交通流短時(shí)預(yù)測(cè)研究進(jìn)展[J];城市交通;2012年06期

7 于濱;鄔珊華;王明華;趙志宏;;K近鄰短時(shí)交通流預(yù)測(cè)模型[J];交通運(yùn)輸工程學(xué)報(bào);2012年02期

8 丁世飛;齊丙娟;譚紅艷;;支持向量機(jī)理論與算法研究綜述[J];電子科技大學(xué)學(xué)報(bào);2011年01期

9 姚智勝;邵春福;;基于狀態(tài)空間模型的道路交通狀態(tài)多點(diǎn)時(shí)間序列預(yù)測(cè)[J];中國(guó)公路學(xué)報(bào);2007年04期

10 楊兆升;王媛;管青;;基于支持向量機(jī)方法的短時(shí)交通流量預(yù)測(cè)方法[J];吉林大學(xué)學(xué)報(bào)(工學(xué)版);2006年06期

相關(guān)博士學(xué)位論文 前3條

1 傅貴;城市智能交通動(dòng)態(tài)預(yù)測(cè)模型的研究及應(yīng)用[D];華南理工大學(xué);2014年

2 崔立成;基于多斷面信息的城市道路網(wǎng)交通流預(yù)測(cè)方法研究[D];大連海事大學(xué);2012年

3 李星毅;基于相似性的交通流分析方法[D];北京交通大學(xué);2010年

相關(guān)碩士學(xué)位論文 前1條

1 劉海紅;交通流狀態(tài)非參數(shù)辨識(shí)關(guān)鍵理論及方法研究[D];山東理工大學(xué);2007年

，

本文編號(hào)：2498694