發(fā)布時間：2018-07-02 19:24

  本文選題：交通流預(yù)測 + 低階非線性變換��； 參考：《天津大學(xué)》2014年碩士論文

【摘要】：隨著社會的進(jìn)步和經(jīng)濟(jì)的發(fā)展,人們對于交通的需求日益增長。但是,現(xiàn)有交通道路的狀況與交通流量的增長之間的矛盾與日俱增。這就需要一個可以對交通流量進(jìn)行實(shí)時、準(zhǔn)確的預(yù)測,用來實(shí)現(xiàn)對交通的控制和誘導(dǎo)。經(jīng)過理論研究和實(shí)踐表明,智能交通系統(tǒng)可以較好的解決這一問題,其主要的方法是基于交通流參數(shù)預(yù)測和交通事件檢測。所以,找到交通流中較好的參數(shù)和交通事件中較好的檢測方法是亟待解決的問題。本論文的主要研究內(nèi)容和成果如下:(1)將一種低階非線性變換(對數(shù)變換)運(yùn)用到小波閾值去噪過程中。在研究中,通過對標(biāo)準(zhǔn)信號加上異常點(diǎn)或者跳躍點(diǎn),不采用對數(shù)變換時,抑制異常值的范圍是[-0.7340,0.4314];經(jīng)過對數(shù)變換后,使得抑制異常點(diǎn)的范圍變化為[-0.7428,0.4685]。區(qū)間長度從1.1654增加到1.2113,增加了3.94%,說明通過非線性變換后,對小波閾值去噪有一定效果,并針對這一現(xiàn)象進(jìn)行了進(jìn)一步的理論分析。該研究屬于“機(jī)理+辨識”預(yù)測策略中的非平穩(wěn)數(shù)據(jù)的平穩(wěn)化方法研究。(2)給出了對數(shù)變換提高小波閾值去噪效果的數(shù)學(xué)分析,并通過對數(shù)變換下的泰勒展開式反映了對數(shù)變換對于小波閾值去噪的影響,即:對數(shù)變換降低非平穩(wěn)時間序列預(yù)測的均方根誤差(MSE,Mean Squared Error),但引起平均誤差(ME,Mean Error)輕微的增加。并且使用數(shù)值試驗(yàn)和公路交通流預(yù)測的實(shí)例對其進(jìn)行了驗(yàn)證與分析。(3)通過對交通流信號進(jìn)行去噪的新舊方法對比,和對五種預(yù)測模型直接進(jìn)行預(yù)測和經(jīng)過非線性變換之后再進(jìn)行預(yù)測兩種方法的對比,說明非線性變換對于小波去噪是有良好效果的。經(jīng)過非線性變換之后再進(jìn)行去噪,使得信號得到了較大的改善。
[Abstract]:With the progress of society and the development of economy, people's demand for transportation is increasing day by day. However, the contradiction between the existing traffic conditions and the growth of traffic flow is increasing day by day. This requires a real-time and accurate prediction of traffic flow, which can be used to control and guide traffic. The theoretical research and practice show that the intelligent transportation system can solve this problem well. The main methods are based on traffic flow parameter prediction and traffic event detection. Therefore, it is an urgent problem to find better parameters in traffic flow and better detection methods in traffic events. The main contents and achievements of this thesis are as follows: (1) A low order nonlinear transform (logarithmic transform) is applied to the wavelet threshold denoising process. In the study, the range of suppression outliers is [-0.7340 ~ 0.4314] by adding outliers or jumping points to the standard signals, and the range of the suppressed outliers is [-0.7428 (0.4685)] after logarithmic transformation. The interval length is increased from 1.1654 to 1.2113, and 3.94 is added. It shows that the wavelet threshold de-noising is effective after nonlinear transformation, and the further theoretical analysis is carried out in view of this phenomenon. This research belongs to the stationary method of non-stationary data in the prediction strategy of "mechanism identification". (2) the mathematical analysis of logarithmic transformation to improve the denoising effect of wavelet threshold is given. The Taylor expansion under logarithmic transformation reflects the effect of logarithmic transformation on wavelet threshold denoising, that is, the logarithmic transformation reduces the MSEM mean square error of the prediction of non-stationary time series, but causes a slight increase in the mean error. Numerical experiments and highway traffic flow prediction examples are used to verify and analyze it. (3) by comparing the new and old methods of traffic flow signal denoising, Compared with the two methods of forecasting five prediction models directly and after nonlinear transformation, it is shown that nonlinear transform has good effect on wavelet denoising. After nonlinear transformation, the signal is de-noised, and the signal is improved greatly.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.112

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本文編號：2090667