本文選題：交通流　切入點(diǎn)：數(shù)據(jù)挖掘　出處：《華南理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：城市快速路通過改善道路條件和交通環(huán)境,以大容量、高效率的交通功能為人們出行提供了快速、高效的運(yùn)行環(huán)境,具有良好的社會效益和經(jīng)濟(jì)效益。目前，我國關(guān)于城市快速路的交通流基礎(chǔ)理論研究正處于發(fā)展階段，國內(nèi)學(xué)者多利用數(shù)學(xué)模型來描述交通狀態(tài)及其變化，并已取得一定成果。但在基于實(shí)證法的交通流研究中，國內(nèi)研究還較少涉及。為此，論文結(jié)合數(shù)據(jù)挖掘和實(shí)證方法探究了城市快速路交通流運(yùn)作機(jī)理，建立了基于交通參數(shù)統(tǒng)計(jì)特性、PCA和BP神經(jīng)網(wǎng)絡(luò)的交通相辨識與預(yù)測模型，為城市交通系統(tǒng)管理提供理論支持和應(yīng)用參考。 本文首先通過交通參數(shù)的時(shí)序圖、基本圖和時(shí)空數(shù)據(jù)重構(gòu)法，定性分析交通流相態(tài)及其演變的數(shù)據(jù)特征和擁擠蔓延特性；隨后論文利用統(tǒng)計(jì)原理，以O(shè)cc為自變量分析了Q、V、ht及其時(shí)間差分量的標(biāo)準(zhǔn)差變化特性，建立了Q-Occ判別相圖，擬合出了F-S相變和S-J相變的S型概率曲線，實(shí)現(xiàn)了定性分析到定量分析的轉(zhuǎn)化；最后，論文利用PCA和KNN方法在低維空間分析了交通相的數(shù)據(jù)特征，確定了Q、V、Occ、ht的特征指標(biāo)，建立了基于PCA和神經(jīng)網(wǎng)絡(luò)的交通相辨識與預(yù)測模型，作為統(tǒng)計(jì)模型的補(bǔ)充。 論文的創(chuàng)新點(diǎn)主要有： （1）根據(jù)城市快速路交通流實(shí)測數(shù)據(jù)，詳細(xì)闡述了城市快速路交通流運(yùn)行機(jī)理，利用統(tǒng)計(jì)原理建立了Q-Occ判別相圖，擬合出了F-S相變和S-J相變的概率曲線； （2）通過PCA和KNN的方法確定了適用于特征提取的交通參數(shù)集合,設(shè)計(jì)了基于PCA特征提取的交通相辨識模型和BP神經(jīng)網(wǎng)絡(luò)的相演變預(yù)測模型。根據(jù)實(shí)測數(shù)據(jù)的檢驗(yàn)，該模型能較好地判別出當(dāng)前交通狀態(tài)，并且能通過“三相匹配曲線”展現(xiàn)三相交通流的博弈過程。三相匹配曲線一定程度上量化了交通相的動態(tài)演變過程，并結(jié)合BP神經(jīng)網(wǎng)絡(luò)應(yīng)用在交通相演變預(yù)測模型。 論文中涉及到的交通流機(jī)理分析、交通相辨識模型和相演變預(yù)測模型，可為智能交通系統(tǒng)的控制策略提供一定的理論依據(jù)和應(yīng)用參考。
[Abstract]:By improving the road conditions and traffic environment, urban expressway provides a fast and efficient environment for people to travel with large capacity and high efficiency. It has good social and economic benefits. The research on the basic theory of urban expressway traffic flow in China is in the developing stage. Domestic scholars often use mathematical models to describe the traffic state and its changes, and have achieved certain results. However, in the research of traffic flow based on empirical method, Therefore, the paper studies the operation mechanism of urban expressway traffic flow based on data mining and empirical methods, and establishes a traffic phase identification and prediction model based on PCA and BP neural network, which is based on the statistical characteristics of traffic parameters. It provides theoretical support and application reference for urban traffic system management. In this paper, firstly, we qualitatively analyze the data characteristics of traffic flow phase and its evolution and congestion spread by using the sequential diagram of traffic parameters, the basic map and the method of reconstruction of spatiotemporal data, and then use the statistical principle to analyze the phase behavior of traffic flow and the characteristics of traffic congestion spread. Using Occ as an independent variable, the variation characteristics of the standard deviation of Q / V _ (t) and its time difference component are analyzed, and the Q-Occ discriminant phase diagram is established, and the S-type probability curves of F-S phase transition and S-J phase transition are fitted out, and the transformation from qualitative analysis to quantitative analysis is realized. In this paper, the PCA and KNN methods are used to analyze the data features of traffic phase in low dimensional space, and the characteristic indexes of QTV-Occht are determined. A traffic phase identification and prediction model based on PCA and neural network is established, which is used as a supplement to the statistical model. The innovations of this paper are as follows:. 1) based on the measured data of urban expressway traffic flow, the operation mechanism of urban expressway traffic flow is expounded in detail, the Q-Occ discriminant phase diagram is established by using the statistical principle, and the probability curves of F-S phase transition and S-J phase transition are fitted out. (2) the set of traffic parameters suitable for feature extraction is determined by means of PCA and KNN, and the traffic phase identification model based on PCA feature extraction and the phase evolution prediction model of BP neural network are designed. The model can distinguish the current traffic state and show the game process of the three-phase traffic flow through "three-phase matching curve". To some extent, the three-phase matching curve quantifies the dynamic evolution process of the traffic phase. Combined with BP neural network, it is applied to the prediction model of traffic phase evolution. The analysis of traffic flow mechanism, traffic phase identification model and phase evolution prediction model in this paper can provide some theoretical basis and application reference for intelligent transportation system control strategy.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.112

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 翁小雄;翁丹;葉麗萍;;城市交通系統(tǒng)的降維狀態(tài)判別規(guī)則[J];華南理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2008年02期

2 黃玲;徐建閩;;基于浮動車技術(shù)的動態(tài)交通擁擠預(yù)測模型[J];華南理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2008年10期

3 姜桂艷,Q，

本文編號：1644035