發(fā)布時間：2018-05-23 08:01

  本文選題：交通流 + 混沌��； 參考：《重慶大學(xué)》2014年碩士論文

【摘要】：對高速公路交通流量實(shí)時準(zhǔn)確的預(yù)測是制定合理的交通管理調(diào)控方案，有效地誘導(dǎo)出行者合理選擇行駛道路的重要前提。高速公路系統(tǒng)是個復(fù)雜的開放系統(tǒng)，其交通流表現(xiàn)出周期性和不確定性共存的特性，而這恰恰正是以非線性和自相似性為特征的混沌系統(tǒng)的具體體現(xiàn)。因此，本文引入混沌理論對高速公路交通流特性進(jìn)行研究，建立混沌預(yù)測模型，提高高速公路交通流量預(yù)測精度，進(jìn)而提升對高速公路的誘導(dǎo)調(diào)控能力。 本文在分析高速公路交通流混沌特性的基礎(chǔ)上，通過重構(gòu)相空間，建立高速公路短時流量預(yù)測模型對高速公路交通流量進(jìn)行預(yù)測。本文的具體研究工作總結(jié)如下： ①高速公路交通流混沌特性的識別及其關(guān)聯(lián)性的分析。首先利用最大Lyapunov指數(shù)法和關(guān)聯(lián)維數(shù)法分別驗(yàn)證了高速公路交通流量、速度和占有率時間序列的混沌特性；然后分析了高速公路交通流各參數(shù)的混沌關(guān)聯(lián)性，為第五章建立多參數(shù)預(yù)測模型的研究提供前提支撐。 ②針對很多文獻(xiàn)都一直規(guī)避的基于最大Lyapunov指數(shù)的混沌預(yù)測會出現(xiàn)兩個預(yù)測值的問題引入馬爾科夫鏈改進(jìn)最大Lyapunov指數(shù)的混沌預(yù)測方法。改進(jìn)的方法將時間序列的斜率作為狀態(tài)變量，并根據(jù)馬爾科夫鏈建立狀態(tài)轉(zhuǎn)移矩陣，進(jìn)而判定預(yù)測值演化方向。 ③高速公路交通流系統(tǒng)是個復(fù)雜的混沌系統(tǒng)，僅用流量時間序列重構(gòu)相空間不一定能勾勒出系統(tǒng)完整的混沌特性。針對這種情況，本文首先根據(jù)Bayes估計(jì)理論將高速公路速度和占有率時間序列融合到一個新的相空間，該相空間包涵了速度和占有率的混沌特性；然后以流量相空間的相點(diǎn)作為基礎(chǔ)重構(gòu)分量，輔以新融合相空間的相點(diǎn)作為重構(gòu)變量，，通過條件熵?cái)U(kuò)維的方法將兩個相空間進(jìn)行融合重構(gòu)，進(jìn)而為建立多參數(shù)預(yù)測模型提供簡潔充分的信息。 本文以渝武高速公路交通流量數(shù)據(jù)分別對建立的單參數(shù)預(yù)測模型和多參數(shù)預(yù)測模型進(jìn)行了驗(yàn)證。結(jié)果表明：本文改進(jìn)的最大Lyapunov指數(shù)預(yù)測法具有較高的的有效性和可行性；本文改進(jìn)的多參數(shù)相空間重構(gòu)方法能夠更好地勾勒出系統(tǒng)的混沌特性，并且基于其建立的多參數(shù)預(yù)測模型具有較高的預(yù)測精度。
[Abstract]:The real-time and accurate prediction of expressway traffic flow is an important prerequisite to formulate reasonable traffic management and control scheme and to effectively induce travelers to choose a reasonable road. Expressway system is a complex open system, and its traffic flow is characterized by periodicity and uncertainty, which is exactly the embodiment of chaotic system characterized by nonlinearity and self-similarity. Therefore, this paper introduces chaos theory to study the characteristics of freeway traffic flow, establishes chaotic forecasting model, improves the precision of expressway traffic flow prediction, and then improves the ability of inducing and regulating the expressway. On the basis of analyzing the chaotic characteristics of freeway traffic flow, this paper establishes a short-time expressway traffic flow forecasting model by reconstructing the phase space to predict the expressway traffic flow. The specific research work of this paper is summarized as follows: 1. Identification of chaotic characteristics of freeway traffic flow and analysis of its correlation. Firstly, the chaos characteristics of freeway traffic flow, velocity and occupancy time series are verified by maximum Lyapunov exponent method and correlation dimension method, and then the chaotic correlation of various parameters of freeway traffic flow is analyzed. In the fifth chapter, it provides the premise support for the research of the multi-parameter prediction model. (2) to solve the problem that the chaos prediction based on the maximum Lyapunov exponent has been evaded by many literatures, the Markov chain is introduced to improve the chaos prediction method of the maximum Lyapunov exponent. The improved method takes the slope of the time series as the state variable and establishes the state transition matrix according to Markov chain and then determines the evolution direction of the predicted value. 3 the freeway traffic flow system is a complex chaotic system, only the reconstruction of phase space with flow time series can not outline the complete chaotic characteristics of the system. In this paper, the freeway velocity and occupancy time series are fused into a new phase space according to the Bayes estimation theory. The phase space contains the chaotic characteristics of velocity and occupancy rate. Then the phase points of the flow phase space are taken as the basic reconstruction components, and the phase points of the new fusion phase space are used as the reconstruction variables, and the two phase spaces are fused and reconstructed by the method of conditional entropy expansion. Furthermore, it provides concise and sufficient information for the establishment of multi-parameter prediction model. Based on the traffic flow data of Yuwu Expressway, this paper verifies the single parameter prediction model and the multi parameter prediction model. The results show that the improved maximum Lyapunov exponent prediction method is more effective and feasible, and the improved multi-parameter phase space reconstruction method can better describe the chaotic characteristics of the system. And the multi-parameter prediction model based on it has high prediction accuracy.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491.1

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本文編號：1923922