城市道路短時(shí)交通流動(dòng)態(tài)預(yù)測方法研究與應(yīng)用
發(fā)布時(shí)間：2019-04-24 01:16
【摘要】：實(shí)時(shí)、準(zhǔn)確、動(dòng)態(tài)的短時(shí)交通流預(yù)測一直是智能交通發(fā)展極力追求的目標(biāo)。然而,短時(shí)交通流信息量大,受到不確定噪聲信號(hào)干擾強(qiáng),再加上城市路網(wǎng)復(fù)雜的拓?fù)浣Y(jié)構(gòu),導(dǎo)致如何實(shí)現(xiàn)城市道路短時(shí)交通流動(dòng)態(tài)預(yù)測這一問題一直制約著智能交通長足的發(fā)展。為了解決上述問題許多預(yù)測方法相繼被提出,但其中一些方法都因未考慮短時(shí)交通流不確定干擾因素或者城市路網(wǎng)復(fù)雜性的影響,致使預(yù)測結(jié)果實(shí)時(shí)性與準(zhǔn)確性都不盡理想,也沒有達(dá)到實(shí)際意義上的動(dòng)態(tài)預(yù)測。本文在Mallat算法下對(duì)短時(shí)交通流信號(hào)進(jìn)行小波分解與重構(gòu),在濾掉短時(shí)交通流信息強(qiáng)干擾噪聲信號(hào)后進(jìn)行時(shí)、頻域特性分析,大大提高了短時(shí)交通流信息預(yù)處理速度及精度,再將支持向量回歸預(yù)測模型結(jié)合在城市路網(wǎng)多斷面的預(yù)測思想中,使得城市道路短時(shí)交通流動(dòng)態(tài)預(yù)測更加準(zhǔn)確、實(shí)時(shí)、有效。論文旨在研究短時(shí)交通流有效信息快速、有效提取后進(jìn)行城市路網(wǎng)中短時(shí)交通流精確的動(dòng)態(tài)預(yù)測。論文主要研究工作如下:1.分析了交通流、速度、密度三個(gè)短時(shí)交通流基本參數(shù)的關(guān)系及數(shù)學(xué)模型;歸納了短時(shí)交通流基本特性;從空間、時(shí)間的層面出發(fā),探討了短時(shí)交通流的相關(guān)性;從動(dòng)力學(xué)特性角度總結(jié)了短時(shí)交通流可預(yù)測性分析方法及其影響因素。2.針對(duì)短時(shí)交通流信號(hào)的強(qiáng)噪聲影響,研究了小波分解與重構(gòu)對(duì)短時(shí)交通流信號(hào)進(jìn)行主體信息和細(xì)節(jié)信息快速、有效提取的關(guān)鍵問題。通過Mallat塔式多分辨率算法思想的引入,實(shí)現(xiàn)了短時(shí)交通流信號(hào)的快速分解與重構(gòu),最后提出了針對(duì)短時(shí)交通流信號(hào)如何有效提取的詳細(xì)解決策略。3.以實(shí)現(xiàn)短時(shí)交通流動(dòng)態(tài)預(yù)測為出發(fā)點(diǎn),給出原始交通流數(shù)據(jù)預(yù)處理的方法,并通過SVR自身特性的研究,提出了模型自適應(yīng)參數(shù)、G-P算法嵌入維數(shù)與核函數(shù)的動(dòng)態(tài)優(yōu)化與選擇方法,使得短時(shí)交通流達(dá)到高效、準(zhǔn)確的動(dòng)態(tài)預(yù)測。4.通過對(duì)城市復(fù)雜網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)的研究,給出了相關(guān)斷面的矩陣表示方法及相互影響權(quán)重的F-AHP模糊標(biāo)定方法;最后結(jié)合前面的研究結(jié)果提出了一個(gè)城市道路短時(shí)交通流動(dòng)態(tài)預(yù)測模型,并通過實(shí)驗(yàn)驗(yàn)證了其合理性。5.利用短時(shí)交通流信號(hào)的Mallat小波分解與重構(gòu)及SVR預(yù)測的研究結(jié)論,在ThinkPHP框架下設(shè)計(jì)并完成了針對(duì)西安市的短時(shí)交通流動(dòng)態(tài)預(yù)測系統(tǒng)的開發(fā)。
[Abstract]:Real-time, accurate and dynamic short-term traffic flow prediction is always the goal of intelligent transportation development. However, short-term traffic flow has a large amount of information and is strongly disturbed by uncertain noise signals, coupled with the complex topological structure of the urban network. The problem of how to realize the dynamic prediction of urban road short-term traffic flow has been restricting the rapid development of intelligent traffic. In order to solve the above problems, many forecasting methods have been put forward one after another, but some of these methods have not considered the influence of uncertain disturbance of short-term traffic flow or the complexity of urban road network, so that the real-time and accuracy of prediction results are not ideal. Also did not achieve the actual meaning of the dynamic prediction. In this paper, the short-time traffic flow signal is decomposed and reconstructed by wavelet transform based on Mallat algorithm. When the short-time traffic flow information is filtered out, the frequency domain characteristic is analyzed, which greatly improves the speed and precision of short-time traffic flow information preprocessing. Then the support vector regression prediction model is combined with the multi-section prediction idea of urban road network, which makes the dynamic prediction of urban road short-term traffic flow more accurate, real-time and effective. The purpose of this paper is to study the short-term traffic flow effective information quickly, and then extract the short-term traffic flow accurately and dynamically forecast the short-term traffic flow in the urban road network. The main research work of this paper is as follows: 1. This paper analyzes the relationship and mathematical model of three basic parameters of short-term traffic flow, such as traffic flow, velocity and density, sums up the basic characteristics of short-term traffic flow, discusses the correlation of short-term traffic flow from the aspect of space and time, discusses the relationship between short-term traffic flow and short-term traffic flow. The predictive analysis method of short-term traffic flow and its influencing factors are summarized from the point of view of dynamic characteristics. Aiming at the influence of strong noise on short-time traffic flow signal, the key problem of fast and efficient extraction of main body information and detail information of short-time traffic flow signal based on wavelet decomposition and reconstruction is studied. Through the introduction of Mallat tower multi-resolution algorithm, the fast decomposition and reconstruction of short-time traffic flow signal is realized. Finally, a detailed solution strategy for how to extract the short-time traffic flow signal effectively is put forward. 3. In order to realize the dynamic prediction of short-term traffic flow, the method of pre-processing the original traffic flow data is given, and the adaptive parameters of the model are put forward through the study of the characteristics of SVR itself. The dynamic optimization and selection method of embedding dimension and kernel function makes the short-term traffic flow achieve efficient and accurate dynamic prediction. 4. Based on the study of the topological structure of urban complex network, the matrix representation method of correlation cross-section and the fuzzy calibration method of F-AHP for mutual influence weights are given. Finally, a dynamic prediction model of urban road short-term traffic flow is proposed based on the previous research results, and its rationality is verified by experiments. Based on the research results of Mallat wavelet decomposition and reconstruction of short-term traffic flow signals and SVR prediction, a short-term traffic flow dynamic prediction system for Xi'an is designed and completed under the framework of ThinkPHP.
【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491.14
本文編號(hào)：2463980
[Abstract]:Real-time, accurate and dynamic short-term traffic flow prediction is always the goal of intelligent transportation development. However, short-term traffic flow has a large amount of information and is strongly disturbed by uncertain noise signals, coupled with the complex topological structure of the urban network. The problem of how to realize the dynamic prediction of urban road short-term traffic flow has been restricting the rapid development of intelligent traffic. In order to solve the above problems, many forecasting methods have been put forward one after another, but some of these methods have not considered the influence of uncertain disturbance of short-term traffic flow or the complexity of urban road network, so that the real-time and accuracy of prediction results are not ideal. Also did not achieve the actual meaning of the dynamic prediction. In this paper, the short-time traffic flow signal is decomposed and reconstructed by wavelet transform based on Mallat algorithm. When the short-time traffic flow information is filtered out, the frequency domain characteristic is analyzed, which greatly improves the speed and precision of short-time traffic flow information preprocessing. Then the support vector regression prediction model is combined with the multi-section prediction idea of urban road network, which makes the dynamic prediction of urban road short-term traffic flow more accurate, real-time and effective. The purpose of this paper is to study the short-term traffic flow effective information quickly, and then extract the short-term traffic flow accurately and dynamically forecast the short-term traffic flow in the urban road network. The main research work of this paper is as follows: 1. This paper analyzes the relationship and mathematical model of three basic parameters of short-term traffic flow, such as traffic flow, velocity and density, sums up the basic characteristics of short-term traffic flow, discusses the correlation of short-term traffic flow from the aspect of space and time, discusses the relationship between short-term traffic flow and short-term traffic flow. The predictive analysis method of short-term traffic flow and its influencing factors are summarized from the point of view of dynamic characteristics. Aiming at the influence of strong noise on short-time traffic flow signal, the key problem of fast and efficient extraction of main body information and detail information of short-time traffic flow signal based on wavelet decomposition and reconstruction is studied. Through the introduction of Mallat tower multi-resolution algorithm, the fast decomposition and reconstruction of short-time traffic flow signal is realized. Finally, a detailed solution strategy for how to extract the short-time traffic flow signal effectively is put forward. 3. In order to realize the dynamic prediction of short-term traffic flow, the method of pre-processing the original traffic flow data is given, and the adaptive parameters of the model are put forward through the study of the characteristics of SVR itself. The dynamic optimization and selection method of embedding dimension and kernel function makes the short-term traffic flow achieve efficient and accurate dynamic prediction. 4. Based on the study of the topological structure of urban complex network, the matrix representation method of correlation cross-section and the fuzzy calibration method of F-AHP for mutual influence weights are given. Finally, a dynamic prediction model of urban road short-term traffic flow is proposed based on the previous research results, and its rationality is verified by experiments. Based on the research results of Mallat wavelet decomposition and reconstruction of short-term traffic flow signals and SVR prediction, a short-term traffic flow dynamic prediction system for Xi'an is designed and completed under the framework of ThinkPHP.
【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491.14
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