【摘要】：近年來(lái),隨著社會(huì)經(jīng)濟(jì)水平不斷的提高,我國(guó)高速公路有了迅猛的發(fā)展,在社會(huì)經(jīng)濟(jì)活動(dòng)中扮演著重要的運(yùn)輸作用,但是頻發(fā)的高速公路交通事故也給社會(huì)經(jīng)濟(jì)帶來(lái)巨大的經(jīng)濟(jì)損失�？焖�、準(zhǔn)確地掌握和估計(jì)高速公路交通流狀態(tài),對(duì)于制定合理、有效的高速公路管控策略具有重要的意義,有利于緩解高速公路交通擁堵和提高高速公路安全性。然而,由于有限的檢測(cè)設(shè)備無(wú)法提供高速公路全方位交通運(yùn)行狀態(tài),因此,本文依托國(guó)家自然科學(xué)基金項(xiàng)目“高速公路車(chē)速離散特征、機(jī)理及控制方法研究”和南京市科技局項(xiàng)目“高速公路交通流狀態(tài)估計(jì)與安全預(yù)警系統(tǒng)”,從實(shí)測(cè)數(shù)據(jù)出發(fā),深入研究高速公路連續(xù)多斷面交通流參數(shù)的時(shí)空關(guān)聯(lián)特性,通過(guò)建立基于卡爾曼濾波的高速公路交通流實(shí)時(shí)狀態(tài)估計(jì)方法,實(shí)時(shí)估計(jì)路段檢測(cè)盲區(qū)的交通流狀態(tài),為制定高效的高速公路管控策略提供理論基礎(chǔ)和技術(shù)支撐。首先,從實(shí)測(cè)數(shù)據(jù)出發(fā),在統(tǒng)計(jì)學(xué)層面上引入交通流參數(shù)時(shí)空相關(guān)系數(shù),分析高速公路多斷面連續(xù)檢測(cè)器交通流參數(shù)的時(shí)間相關(guān)性和空間相關(guān)性,為下文檢測(cè)盲區(qū)的交通流狀態(tài)估計(jì)提供數(shù)據(jù)基礎(chǔ),同時(shí)確定下文模型狀態(tài)估計(jì)的路段區(qū)域長(zhǎng)度。其次,研究不同宏觀交通流模型實(shí)際估計(jì)效果,通過(guò)遺傳算法對(duì)不同宏觀交通流模型參數(shù)進(jìn)行在線標(biāo)定,并將標(biāo)定后的模型應(yīng)用于時(shí)空關(guān)聯(lián)性較強(qiáng)的斷面進(jìn)行狀態(tài)估計(jì),以統(tǒng)計(jì)學(xué)評(píng)價(jià)指標(biāo)最優(yōu)為目標(biāo),選取交通流狀態(tài)估計(jì)的最佳交通流模型；同時(shí)對(duì)模型的參數(shù)進(jìn)行了敏感性分析。進(jìn)一步對(duì)模型精度與檢測(cè)間隔和路段距離的關(guān)系進(jìn)行探討。最終選取Jiang-Zhu-Wu模型作為交通流狀態(tài)估計(jì)模型,其中自由流速度和阻塞傳播速度為模型關(guān)鍵參數(shù),模型在檢測(cè)間隔為30s、路段劃分距離為800m時(shí)狀態(tài)估計(jì)效果最優(yōu)。再次,以Kalman濾波“遞推-估計(jì)-修正”原理為基礎(chǔ),分別構(gòu)建了基于擴(kuò)展Kalman濾波和基于無(wú)跡Kalman濾波的高速公路交通流狀態(tài)估計(jì)模型,并給出狀態(tài)估計(jì)的步驟。最后,基于實(shí)測(cè)數(shù)據(jù)對(duì)所構(gòu)建的的交通流狀態(tài)估計(jì)模型進(jìn)行實(shí)例應(yīng)用并評(píng)價(jià)其效果,主要包括兩種狀態(tài)估計(jì)模型對(duì)交通流狀態(tài)突變的追蹤能力、狀態(tài)估計(jì)誤差的對(duì)比分析等。同時(shí)亦對(duì)不同檢測(cè)器布設(shè)方案下交通流狀態(tài)估計(jì)模型的實(shí)施效果進(jìn)行探討,給出不同布設(shè)方案的誤差,為檢測(cè)器布設(shè)提供參考依據(jù)。
[Abstract]:In recent years, with the continuous improvement of social and economic level, the highway in China has a rapid development, which plays an important role in the social and economic activities. However, frequent highway traffic accidents also bring huge economic losses to the social economy. To grasp and estimate the state of expressway traffic flow quickly and accurately is of great significance for making reasonable and effective expressway management and control strategy, which is helpful to alleviate the traffic congestion and improve the safety of expressway. However, due to the limited detection equipment can not provide the highway traffic operation state, so this paper relies on the National Natural Science Foundation project "Highway speed discrete characteristics," The research on mechanism and control method "and Nanjing Science and Technology Bureau project" Expressway Traffic flow State estimation and Safety early warning system ", based on the measured data, the spatial-temporal correlation characteristics of continuous multi-section traffic flow parameters of expressway are deeply studied. By establishing the real-time state estimation method of freeway traffic flow based on Kalman filter, the real-time estimation of the traffic flow in the blind area of highway detection is carried out in real time, which provides the theoretical basis and technical support for the formulation of efficient expressway management and control strategy. Firstly, based on the measured data, the spatial-temporal correlation coefficient of traffic flow parameters is introduced to analyze the temporal and spatial correlation of traffic flow parameters of multi-section continuous detector in freeway. It provides a data basis for the traffic flow state estimation in the following blind areas, and determines the length of the road segment estimated by the following model state. Secondly, the actual estimation effect of different macroscopic traffic flow models is studied. The parameters of different macroscopic traffic flow models are calibrated online by genetic algorithm, and the calibrated models are applied to estimate the state of sections with strong temporal and spatial correlation. The optimal traffic flow model of traffic flow state estimation is selected. At the same time, the sensitivity of the model parameters is analyzed. The relationship between the accuracy of the model and the detection interval and the distance between the sections is discussed. Finally, the Jiang-Zhu-Wu model is selected as the traffic flow state estimation model, in which the free flow velocity and the congestion propagation velocity are the key parameters of the model. The model has the best effect when the detection interval is 30s and the partition distance is 800m. Thirdly, based on the principle of "Recursion-Estimator-Correction" of Kalman filter, the traffic flow state estimation models based on extended Kalman filter and unscented Kalman filter are constructed, and the steps of state estimation are given. Finally, the traffic flow state estimation model is applied to the traffic flow estimation model based on the measured data and its effect is evaluated, including the tracking ability of the two state estimation models to the sudden change of the traffic flow state, the comparative analysis of the state estimation error, and so on. At the same time, the effect of traffic flow state estimation model under different detector layout schemes is discussed, the error of different layout schemes is given, and the reference basis for detector layout is provided.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：U491.112

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