【摘要】：隨著機(jī)動(dòng)車數(shù)量的不斷增加,國內(nèi)外大部分城市道路以及高速公路的交通擁堵狀況日益嚴(yán)峻,這種現(xiàn)象嚴(yán)重的影響到了人們的日常工作和生活。為解決交通擁堵問題,智能交通系統(tǒng)被廣泛采用并有效的緩解了擁堵的狀況。由于交通數(shù)據(jù)的采集技術(shù)不斷進(jìn)步,使大量的歷史數(shù)據(jù)作為交通狀態(tài)的預(yù)測樣本成為了可能。交通狀態(tài)預(yù)測是智能交通系統(tǒng)進(jìn)行交通管理中很重要的一部分,是交通誘導(dǎo)的前提。所以,交通狀態(tài)預(yù)測的研究對交通規(guī)劃及交通的優(yōu)化控制有非常重要的作用。本文選擇反映交通狀態(tài)最直接的參數(shù)速度來作為狀態(tài)預(yù)測的參數(shù)。針對現(xiàn)有速度預(yù)測方法的不足,提出了基于KNN-HA和KNN-RBF相融合的速度預(yù)測模型。首先使用KNN-HA方法和KNN-RBF方法對預(yù)測路段的速度做預(yù)測,分別得到周內(nèi)和周末的預(yù)測結(jié)果。根據(jù)早晚高峰將一天分為5個(gè)時(shí)間段,比較每個(gè)時(shí)間段內(nèi)兩種方法的預(yù)測精度,得出了基于兩種算法相融合的速度預(yù)測算法;其次將本文的方法與神經(jīng)網(wǎng)絡(luò)算法(NN)和支持向量回歸算法(SVR)等經(jīng)典方法進(jìn)行比較,得出本文提出的預(yù)測模型優(yōu)于其他預(yù)測模型,預(yù)測精度比支持向量回歸算法提高了11%,比KNN-RBF算法提高了6%;最后根據(jù)速度閾值將交通狀態(tài)劃分為5個(gè)狀態(tài),以預(yù)測速度對道路的交通狀態(tài)進(jìn)行判斷,并比較了預(yù)測值和實(shí)際交通狀態(tài)的一致性,預(yù)測精度達(dá)到91.7%。
[Abstract]:With the increase of the number of motor vehicles, the traffic congestion of most urban roads and highways is becoming more and more serious at home and abroad, which seriously affects the daily work and life of people. In order to solve the problem of traffic congestion, Intelligent Transportation system (its) has been widely used and effectively alleviated the congestion. Because of the continuous progress of traffic data acquisition technology, a large number of historical data as traffic state prediction samples become possible. Traffic state prediction is an important part of intelligent transportation system in traffic management and the premise of traffic guidance. Therefore, the study of traffic state prediction plays an important role in traffic planning and traffic optimization control. In this paper, the most direct parameter speed which reflects the traffic state is chosen as the parameter of state prediction. A speed prediction model based on the fusion of KNN-HA and KNN-RBF is proposed to overcome the shortcomings of existing speed prediction methods. First, the KNN-HA method and the KNN-RBF method are used to predict the speed of the predicted section, and the results of the prediction are obtained at the end of the week and the weekend, respectively. According to the morning and evening peak, the day is divided into five time periods, and the prediction accuracy of the two methods in each time period is compared, and the speed prediction algorithm based on the fusion of the two algorithms is obtained. Secondly, compared with the classical methods such as neural network algorithm (NN) and support vector regression algorithm (SVR), the prediction model proposed in this paper is superior to other prediction models. The accuracy of prediction is 11% higher than that of support vector regression algorithm and 6% higher than that of KNN-RBF algorithm. Finally, the traffic state is divided into 5 states according to the speed threshold, and the traffic state is judged by forecasting speed. The consistency between the predicted value and the actual traffic state is compared, and the prediction accuracy is 91.7%.
【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U491

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本文編號：2265575