本文關(guān)鍵詞：高速公路基本路段實(shí)時(shí)交通狀態(tài)判別方法的研究及應(yīng)用　出處：《長安大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 高速公路基本路段 交通狀態(tài)判別 模糊聚類 遺傳算法 核極限學(xué)習(xí)機(jī)

【摘要】：目前高速公路交通狀態(tài)的判別標(biāo)準(zhǔn)大多以固定閾值比較或絕對(duì)標(biāo)準(zhǔn)為主,忽略了不同時(shí)空背景下道路環(huán)境、天氣狀況等客觀因素的影響。為此,本文以高速公路基本路段為研究對(duì)象,依據(jù)交通流具有時(shí)間序列相似性,利用海量的交通流表征參數(shù)運(yùn)行數(shù)據(jù),構(gòu)建出交通狀態(tài)劃分的相對(duì)標(biāo)準(zhǔn)及實(shí)時(shí)交通狀態(tài)判別決策模型。對(duì)高速公路交通流的控制與管理,具有重要的研究意義與應(yīng)用價(jià)值。本文首先研究了交通流運(yùn)行和擁擠特性,提取交通狀態(tài)表征參數(shù),同時(shí)針對(duì)交通狀態(tài)判別采用絕對(duì)標(biāo)準(zhǔn)的缺陷,提出了基于模糊聚類的高速公路基本路段實(shí)時(shí)交通狀態(tài)劃分的相對(duì)標(biāo)準(zhǔn)的思路。其次,基于模糊C均值聚類算法對(duì)歷史交通流運(yùn)行數(shù)據(jù)進(jìn)行聚類分析,將獲取的交通狀態(tài)聚類中心作為劃分的相對(duì)標(biāo)準(zhǔn),再根據(jù)歐式距離判別實(shí)測數(shù)據(jù)的交通狀態(tài)。針對(duì)模糊C均值聚類判別算法中初始聚類中心選取具有隨機(jī)性使得算法不穩(wěn)定、易陷入局部最優(yōu)的問題,引入遺傳算法對(duì)初始聚類中心選取進(jìn)行優(yōu)化,增強(qiáng)交通狀態(tài)聚類分析的可靠性。同時(shí),鑒于歐式距離決策模型計(jì)算時(shí)間復(fù)雜度較大,構(gòu)建了基于核極限學(xué)習(xí)機(jī)的實(shí)時(shí)交通狀態(tài)判別決策模型。從分類性能和計(jì)算時(shí)間復(fù)雜度兩個(gè)角度,將該方法與支持向量機(jī)模型進(jìn)行對(duì)比分析。最后,以PeMS實(shí)測的交通流表征參數(shù)運(yùn)行數(shù)據(jù)為基礎(chǔ),對(duì)本文所構(gòu)建的高速公路基本路段實(shí)時(shí)交通狀態(tài)判別方法進(jìn)行了仿真分析。結(jié)果表明,基于遺傳算法的模糊C均值聚類算法穩(wěn)定性好,收斂速度快;基于核極限學(xué)習(xí)機(jī)的實(shí)時(shí)交通狀態(tài)決策模型在保證分類精度的基礎(chǔ)上,大大節(jié)省了時(shí)間成本,具有較好的實(shí)時(shí)性。
[Abstract]:At present, the criterion of freeway traffic state is mostly fixed threshold comparison or absolute standard, ignoring the influence of road environment, weather condition and other objective factors in different time and space background. This paper takes the basic section of highway as the research object, according to the traffic flow has the time series similarity, uses massive traffic flow to represent the parameter running data. The relative standard of traffic state division and the decision model of real-time traffic state discrimination are constructed. The control and management of expressway traffic flow are also given. It has important research significance and application value. Firstly, this paper studies the traffic flow and congestion characteristics, extracts the parameters of traffic state representation, and at the same time uses the absolute standard to judge the traffic state. This paper puts forward the idea of relative standard of real-time traffic state partition of the basic section of highway based on fuzzy clustering. Secondly, the paper analyzes the running data of historical traffic flow based on fuzzy C-means clustering algorithm. The traffic state clustering center is regarded as the relative criterion of the partition. Then according to the Euclidean distance to judge the traffic state of the measured data, the random selection of the initial clustering center in the fuzzy C-means clustering discriminant algorithm makes the algorithm unstable and easy to fall into the local optimal problem. Genetic algorithm is introduced to optimize the selection of initial clustering centers to enhance the reliability of traffic state clustering analysis. At the same time, the computational time complexity of Euclidean distance decision model is large. A real-time traffic state discriminant decision model based on kernel extreme learning machine (KLMs) is constructed. From the perspective of classification performance and computational time complexity, the method is compared with the SVM model. Finally, the proposed method is compared with the SVM model. Based on the operation data of the traffic flow parameters measured by PeMS, the real-time traffic state discrimination method of the basic section of expressway is simulated and analyzed. The results show that the method is effective. The fuzzy C-means clustering algorithm based on genetic algorithm has good stability and fast convergence speed. The real-time traffic state decision model based on kernel limit learning machine can greatly save the time cost and have better real-time performance on the basis of ensuring the classification accuracy.
【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

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相關(guān)期刊論文 前3條

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本文編號(hào)：1419063