【摘要】：摘要：交通流數(shù)據(jù)是交通狀態(tài)辨識(shí)、交通管理及控制等交通領(lǐng)域研究及工作開展的基礎(chǔ)。隨著交通信息采集系統(tǒng)的發(fā)展,海量交通流數(shù)據(jù)不斷涌現(xiàn),但由于檢測(cè)器自身故障、傳輸網(wǎng)絡(luò)故障及環(huán)境因素等的影響,采集到的交通流數(shù)據(jù)難免會(huì)出現(xiàn)各種質(zhì)量問題(不完整、錯(cuò)誤、噪音等)。有效地對(duì)交通流故障數(shù)據(jù)(包括缺失數(shù)據(jù)和異常數(shù)據(jù))進(jìn)行識(shí)別和修復(fù),使其能夠真實(shí)地反映交通運(yùn)行狀態(tài),才能為后續(xù)各項(xiàng)研究的順利開展提供完整的數(shù)據(jù)支持和基礎(chǔ)保障。 本論文從檢測(cè)器采集到的交通流數(shù)據(jù)出發(fā),在對(duì)故障數(shù)據(jù)進(jìn)行有效識(shí)別及分析的基礎(chǔ)上研究對(duì)故障數(shù)據(jù)進(jìn)行修復(fù)的方法。首先對(duì)數(shù)據(jù)進(jìn)行時(shí)空特性分析,確定用來進(jìn)行故障數(shù)據(jù)修復(fù)的時(shí)間特征參數(shù)和空間特征參數(shù)；然后進(jìn)一步分析了交通故障數(shù)據(jù)識(shí)別和修復(fù)的方法,提出了基于平滑估計(jì)閾值的故障數(shù)據(jù)識(shí)別方法和基于統(tǒng)計(jì)相關(guān)分析的故障數(shù)據(jù)修復(fù)方法,并進(jìn)行了實(shí)驗(yàn)驗(yàn)證；最后基于“分解—組合”思想,以基于最小二乘支持向量機(jī)的數(shù)據(jù)修復(fù)模型作為局部修復(fù)模型,以基于最大熵的交通狀態(tài)概率分布估計(jì)模型作為自適應(yīng)權(quán)重模型,提出一種自適應(yīng)權(quán)重的兩階段故障數(shù)據(jù)修復(fù)組合模型,并結(jié)合北京市微波檢測(cè)器的實(shí)際交通流量數(shù)據(jù)進(jìn)行實(shí)驗(yàn)驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,該方法能夠較大程度上減少交通系統(tǒng)隨機(jī)因素的干擾,具有較高的修復(fù)精度。
[Abstract]:Abstract: traffic flow data is the basis of traffic state identification, traffic management and control. With the development of traffic information collection system, massive traffic flow data continue to emerge, but due to the fault of detector itself, transmission network fault and environmental factors, the collected traffic flow data will inevitably have various quality problems (incomplete, error, noise, etc.). Effectively identify and repair the traffic flow fault data (including missing data and abnormal data), so that it can truly reflect the traffic operation state, in order to provide complete data support and basic support for the smooth development of the follow-up research. Based on the traffic flow data collected by the detector, this paper studies the method of repairing the fault data on the basis of the effective identification and analysis of the fault data. Firstly, the temporal and spatial characteristic parameters used to repair the fault data are determined, and then the methods of traffic fault data identification and repair are further analyzed, and the fault data recognition method based on smoothing estimation threshold and the fault data repair method based on statistical correlation analysis are proposed and verified by experiments. Finally, based on the idea of "decomposition-combination", taking the data repair model based on least square support vector machine as the local repair model and the traffic state probability distribution estimation model based on maximum entropy as the adaptive weight model, a two-stage fault data repair combination model with adaptive weight is proposed, and the experimental verification is carried out with the actual traffic flow data of Beijing microwave detector. The experimental results show that the method can greatly reduce the interference of random factors in traffic system and has high repair accuracy.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491.112

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陸化普;大城市交通問題的癥結(jié)與出路[J];城市發(fā)展研究;1997年05期

2 王建;鄧衛(wèi);趙金寶;;基于改進(jìn)型貝葉斯組合模型的短時(shí)交通流量預(yù)測(cè)[J];東南大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年01期

3 夏冰,董菁,張佐;周相似特性下的交通流預(yù)測(cè)模型研究[J];公路交通科技;2003年02期

4 李素建;劉群;張志勇;程學(xué)旗;;語言信息處理技術(shù)中的最大熵模型方法[J];計(jì)算機(jī)科學(xué);2002年07期

5 徐健銳;李星毅;施化吉;;處理缺失數(shù)據(jù)的短時(shí)交通流預(yù)測(cè)模型[J];計(jì)算機(jī)應(yīng)用;2010年04期

6 姜桂艷,Q，

本文編號(hào)：2506880