【摘要】：短期交通流預(yù)測是智能交通的熱點研究領(lǐng)域,短期交通流預(yù)測是交通控制和車載導(dǎo)航的重要問題之一,也是智能交通控制和交通誘導(dǎo)的關(guān)鍵技術(shù)之一。短期交通流具有非線性、時變性、不確定性、不穩(wěn)定性等特點,以各種預(yù)測模型實現(xiàn)短時交通流預(yù)測,可以緩解城市交通擁堵,避免社會資源的浪費。因此,研究短時交通流的預(yù)測具有重要的現(xiàn)實意義和應(yīng)用價值。 對于非線性時間序列的交通流預(yù)測,采用RBF神經(jīng)網(wǎng)絡(luò)具有較好的效果,其預(yù)測準(zhǔn)確度也較高,但RBF神經(jīng)網(wǎng)絡(luò)具有泛化較差的缺點。 為了克服RBF神經(jīng)網(wǎng)絡(luò)的缺點,采用支持向量機對交通流進行預(yù)測,但是支持向量機僅對200個以下的小樣本數(shù)據(jù)可以獲得較好的預(yù)測結(jié)果,而交通流的數(shù)據(jù)樣本一般在上千個左右,故用支持向量機對交通流進行預(yù)測的誤差仍然比較大。 為了克服RBF神經(jīng)網(wǎng)絡(luò)和支持向量機各自的缺點,本文把這兩個模型組合使用,根據(jù)不同的組合算法達到不同的組合預(yù)測效果。利用權(quán)值計算公式對兩種預(yù)測模型進行權(quán)值計算,通過以往預(yù)測結(jié)果的誤差得出兩種預(yù)測結(jié)果的權(quán)值,從而得到較高的預(yù)測結(jié)果。由于這種權(quán)值計算公式是基于經(jīng)驗計算的統(tǒng)計結(jié)果,其預(yù)測誤差是隨機的、非線性的,于是采用支持向量機來對上述兩個模型的預(yù)測結(jié)果進行二次預(yù)測,讓支持向量機來計算兩種單一預(yù)測模型的權(quán)值,從而得到了更精確的預(yù)測結(jié)果。 實驗結(jié)果表明,采用組合模型的預(yù)測比單一模型的預(yù)測效果更好。相對地,使用支持向量機計算權(quán)值的組合預(yù)測模型要比使用權(quán)值計算公式的組合預(yù)測模型得到的預(yù)測結(jié)果要精確一些,預(yù)測效果更好一些。
[Abstract]:Short-term traffic flow prediction is a hot research field in intelligent transportation. Short-term traffic flow prediction is one of the important problems in traffic control and vehicle navigation, and is also one of the key technologies of intelligent traffic control and traffic guidance. Short-term traffic flow has the characteristics of nonlinearity, time-varying, uncertainty, instability and so on. Using various forecasting models to forecast short-term traffic flow can alleviate urban traffic congestion and avoid the waste of social resources. Therefore, the study of short-time traffic flow prediction has important practical significance and application value. For the traffic flow prediction of nonlinear time series, the RBF neural network has a good effect and its prediction accuracy is higher, but the RBF neural network has the disadvantage of poor generalization. In order to overcome the shortcoming of RBF neural network, support vector machine (SVM) is used to predict traffic flow. The data samples of traffic flow are usually thousands or so, so the error of forecasting traffic flow with support vector machine is still large. In order to overcome the shortcomings of RBF neural network and support vector machine, this paper combines the two models to achieve different combination prediction results according to different combination algorithms. Weight calculation formula is used to calculate the weights of the two prediction models, and the weight values of the two prediction results are obtained by the error of the previous prediction results, and the higher prediction results are obtained. Because this formula is based on the statistical results of empirical calculation and the prediction error is random and nonlinear, support vector machine (SVM) is used to predict the prediction results of the above two models. Support vector machine (SVM) is used to calculate the weights of two single prediction models, and a more accurate prediction result is obtained. The experimental results show that the combined model is more effective than the single model. Comparatively, the combined prediction model using support vector machine to calculate the weight value is more accurate than the combination prediction model with the formula of the right to use, and the prediction effect is better.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.1;TP183

【參考文獻】

相關(guān)期刊論文 前3條

1 張敬磊;王曉原;;交通流灰色RBF網(wǎng)絡(luò)非線性組合預(yù)測方法[J];數(shù)學(xué)的實踐與認(rèn)識;2011年19期

2 賀國光,馬壽峰,李宇;基于小波分解與重構(gòu)的交通流短時預(yù)測法[J];系統(tǒng)工程理論與實踐;2002年09期

3 譚滿春;馮犖斌;徐建閩;;基于ARIMA與人工神經(jīng)網(wǎng)絡(luò)組合模型的交通流預(yù)測[J];中國公路學(xué)報;2007年04期

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