本文選題：交通狀態(tài)判別 + 支持向量機(jī)　； 參考：《青島科技大學(xué)》2014年碩士論文

【摘要】：道路交通狀態(tài)判別是智能交通管理系統(tǒng)進(jìn)行路況信息發(fā)布和交通誘導(dǎo)的基礎(chǔ)，目前我國(guó)各大城市的交通基礎(chǔ)信息采集系統(tǒng)和交通誘導(dǎo)信息發(fā)布系統(tǒng)建設(shè)均已初具規(guī)模。然而在數(shù)據(jù)處理和實(shí)時(shí)道路交通狀態(tài)判別中卻還存在一些問(wèn)題，亟待改進(jìn)。交通狀態(tài)判別是交通誘導(dǎo)中一個(gè)很重要的問(wèn)題，提供實(shí)時(shí)準(zhǔn)確的交通狀態(tài)信息能為出行者做出合理的路徑選擇提供參考。因此，需要對(duì)路網(wǎng)中的交通狀態(tài)信息做出準(zhǔn)確判別預(yù)測(cè)。本文重點(diǎn)研究交通狀態(tài)判別問(wèn)題。 對(duì)于交通狀態(tài)判別問(wèn)題，考慮到傳統(tǒng)的交通狀態(tài)劃分方法是根據(jù)國(guó)家交通部門(mén)給定的交通狀態(tài)指標(biāo)來(lái)進(jìn)行劃分，這種方法對(duì)于不同道路的適應(yīng)性不是很強(qiáng)，在實(shí)際應(yīng)用中很難滿足準(zhǔn)確性要求。因此，本文采用模糊聚類的方法來(lái)劃分交通狀態(tài)，，對(duì)不同路段做出不同的狀態(tài)劃分。在此基礎(chǔ)上，采用多類支持向量機(jī)方法對(duì)未來(lái)時(shí)刻的交通狀況進(jìn)行分類。多類支持向量機(jī)是傳統(tǒng)兩類支持向量機(jī)的改進(jìn)，能夠?qū)哂卸喾N劃分指標(biāo)的問(wèn)題進(jìn)行很好的劃分，適合進(jìn)行交通狀態(tài)判別。 本文提出了實(shí)時(shí)的交通狀態(tài)判別系統(tǒng)模型。系統(tǒng)根據(jù)實(shí)時(shí)采集的交通參數(shù)信息準(zhǔn)確的判斷未來(lái)時(shí)刻的交通狀態(tài)信息，并及時(shí)地將交通狀態(tài)信息進(jìn)行發(fā)布，很好地實(shí)現(xiàn)了交通誘導(dǎo)，提高了交通系統(tǒng)的服務(wù)質(zhì)量。
[Abstract]:Road traffic condition identification is the basis of traffic information release and traffic guidance in intelligent traffic management system. At present, the construction of traffic basic information collection system and traffic guidance information publishing system in major cities in China has begun to take shape. However, there are still some problems in data processing and real-time traffic condition discrimination, which need to be improved. Traffic condition discrimination is an important problem in traffic guidance. Providing real-time and accurate traffic state information can provide a reference for travelers to make reasonable path selection. Therefore, it is necessary to make accurate discrimination and prediction of traffic state information in road network. This paper focuses on the problem of traffic state discrimination. For the problem of traffic condition discrimination, considering that the traditional traffic state classification method is based on the traffic state index given by the national transportation department, the adaptability of this method to different roads is not very strong. It is difficult to meet the requirement of accuracy in practical application. Therefore, this paper uses fuzzy clustering method to divide traffic state and make different state partition for different road sections. On this basis, multi-class support vector machine (SVM) method is used to classify traffic conditions in the future. Multi-class support vector machine (SVM) is an improvement of traditional two kinds of SVM. It can well divide the problems with multiple partitioning indexes and is suitable for traffic condition discrimination. In this paper, a real-time traffic condition discriminant system model is proposed. The system can accurately judge the traffic state information of the future time according to the traffic parameter information collected in real time, and publish the traffic state information in time, which realizes the traffic guidance and improves the service quality of the traffic system.
【學(xué)位授予單位】：青島科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491;TP181

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