本文選題：Hadoop + BP神經(jīng)網(wǎng)絡(luò)算法�。� 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：隨著交通系統(tǒng)復(fù)雜性程度日益提高,盡管相關(guān)部門在各種交通設(shè)施建設(shè)方面投入了大量的資金和資源,但其仍然不能滿足人們的出行要求。面對如此復(fù)雜的交通系統(tǒng),為了提高智能交通系統(tǒng)的搜索效率,縮減其搜索范圍,在更短的時間內(nèi)反饋路網(wǎng)信息,縮短用戶在出行過程中“無謂的等待時間”,對短時交通流預(yù)測和路徑誘導(dǎo)算法的研究是很有必要的。然而要解決上述問題,最重要的是提高短時交通流預(yù)測以及路徑誘導(dǎo)算法的效率。從短時交通流預(yù)測算法、路徑誘導(dǎo)算法的角度來看,交通管理的成效,和預(yù)測、誘導(dǎo)精度以及算法效率直接相關(guān)。但是,一般來說,精度和效率之間呈負(fù)相關(guān)關(guān)系,算法的精度越高,代表其邏輯太過復(fù)雜,或者是計算工作量大,因此計算耗時更長,效率更低,嚴(yán)重時徹底失去實用性。在短時交通流預(yù)測方面,論文中對常用短時交通流預(yù)測算法進(jìn)行了分析對比,并指出各種算法的優(yōu)缺點及使用范圍,因BP神經(jīng)網(wǎng)絡(luò)算法構(gòu)建的數(shù)學(xué)模型具有十分嚴(yán)謹(jǐn)?shù)奶攸c,同時具有自主學(xué)習(xí)能力、良好的容錯能力以及良好的泛化性,所以選取BP神經(jīng)網(wǎng)絡(luò)算法對短時交通流進(jìn)行預(yù)測研究。但是BP神經(jīng)網(wǎng)絡(luò)算法因采用靜態(tài)梯度下降法來優(yōu)化網(wǎng)絡(luò)權(quán)值和閾值,使其BP神經(jīng)網(wǎng)絡(luò)算法存在一定的局限性,如穩(wěn)定較差,收斂速率緩慢,容易達(dá)到局部極小值等缺陷。為了克服上述缺陷,論文在短時交通流預(yù)測中采用改進(jìn)后的遺傳算法來優(yōu)BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型。遺傳算法作為一種全局范圍的搜索算法,通過模擬遺傳過程中遺傳因子復(fù)制、交叉和變異的特性,對個體不斷進(jìn)行擇優(yōu),將最終得到的最優(yōu)解作為神經(jīng)網(wǎng)絡(luò)算法的初始值。但是交通流數(shù)據(jù)的復(fù)雜多樣性使得遺傳算法在搜索的過程中可能存在最優(yōu)解丟失的情況,從而導(dǎo)致算法過早收斂,反而降低了短時交通流預(yù)測的準(zhǔn)確性。為了克服以上缺陷,在遺傳算法中引入跟短時交通流運動極其匹配的混沌現(xiàn)象,組成混沌遺傳算法(CGA)。其核心思想主要是在待優(yōu)化變量中引入混沌狀態(tài),并把混沌運動的遍歷范圍“擴(kuò)展”至待優(yōu)化變量的取值范圍中,進(jìn)行全局細(xì)化搜索,這樣就能避免過早陷入局部最優(yōu)解,最終通過不斷優(yōu)化得到最優(yōu)解。然后用得到的最優(yōu)解初始化BP神經(jīng)網(wǎng)絡(luò)的初始權(quán)值和閾值,從而提高對短時交通流預(yù)測的實效性和準(zhǔn)確度。論文并驗證了改進(jìn)后的算法其性能比之前有明顯提高。在路徑誘導(dǎo)方面,論文中也是對常用路徑誘導(dǎo)算法進(jìn)行了研究對比,并分析出各自算法的優(yōu)缺點和使用范圍,由于蟻群算法具有智能化搜索,能夠達(dá)到全局優(yōu)化的目的,在魯棒性、自組織性、并行性方面表現(xiàn)十分突出,并且適合復(fù)雜的非線性交通系統(tǒng)中,所以采用蟻群算法對路徑誘導(dǎo)進(jìn)行研究。當(dāng)然,任何算法都會有自身的局限性和不足,論文針對蟻群算法上存在的缺陷分別對蟻群算法的狀態(tài)轉(zhuǎn)移規(guī)則和信息素更新規(guī)則進(jìn)行了改進(jìn)。從而減少出行用戶對無效路徑的搜索,并且能從綜合因素中選擇最優(yōu)路徑。在本課題的研究中,在滿足短時交通流預(yù)測和路徑誘導(dǎo)算法實用性要求的前提下,充分發(fā)揮云計算平臺在數(shù)據(jù)保存和并行處理方面的優(yōu)勢作用,結(jié)合Hadoop平臺,對改進(jìn)后的BP神經(jīng)網(wǎng)絡(luò)算法和蟻群算法進(jìn)行了MapReduce的設(shè)計和實現(xiàn),成功地設(shè)計出新的短時交通流預(yù)測以及路徑誘導(dǎo)方法,在預(yù)測、誘導(dǎo)的精度和效率之間找到良好的平衡點,大大強(qiáng)化了兩種算法在實用性方面的表現(xiàn),并且在實驗中驗證了算法的性能和實用性。
[Abstract]:With the increasing complexity of traffic system, although the relevant departments invested a lot of money and resources in various transportation facilities, but it still can not meet people's travel requirements. Facing such complicated traffic system, in order to improve the searching efficiency of the intelligent transportation system, reduce the search scope and network feedback information in a shorter period of time in the course of travel, reduce the user in "waiting time", research on short term traffic flow forecasting and route guidance algorithm is very necessary. However, in order to solve the above problems, the most important is to improve the short-term traffic flow prediction and route guidance algorithm. From the short-time traffic flow prediction algorithm, routing algorithm the point of view, the effectiveness of traffic management, and prediction, accuracy and efficiency of the algorithm by directly related. However, in general, showed a negative correlation between accuracy and efficiency The relationship, the higher accuracy of the algorithms, on behalf of the logic is too complex, or the calculation workload, so the computation time is longer, the efficiency is lower, which completely lose the practicability. In prediction of short-term traffic flow, the paper used the short-time traffic flow prediction algorithm are analyzed, and points out the advantages and disadvantages of various algorithms and the scope of use, due to the construction of mathematical model of BP neural network algorithm has the characteristics of very strict, and has self-learning ability, good tolerance and good generalization, so the selection of the BP neural network algorithm to forecast the short-term traffic flow. But the BP neural network algorithm by using static gradient to optimize the network weights and threshold descent, the BP neural network algorithm has some limitations, such as poor stability, slow convergence rate, easy to reach a local minimum in order to overcome the above defects. The defects of short-term traffic flow prediction using improved genetic algorithm to optimize BP neural network prediction model. The genetic algorithm is a global search algorithm, through the simulation of genetic factor in genetic process, characteristics of crossover and mutation, the ongoing individual merit, finally obtained the optimal solution as the initial nerve the value of the network. But the complexity and diversity of traffic flow data make the genetic algorithm possible loss of optimal solution in the search process, which leads to premature convergence of the algorithm, it reduces the accuracy of short-term traffic flow forecasting. In order to overcome the above defects, introducing chaos phenomena with short-term traffic flow, genetic algorithm in the extreme in the form of chaos genetic algorithm (CGA). The core idea is to be optimized in chaotic state variables, and the chaotic motion of the traverse range expanding The range of variables to be optimized to show "in the global refining search, so you can avoid falling into local optimal solution, finally through continuous optimization to obtain the optimal solution. And then get the optimal solution to initialize BP neural network initial weights and thresholds, so as to improve the effectiveness of short time traffic flow prediction and accuracy. The results show that the improved algorithm has significantly improved its performance than before. In the aspect of the induced path, but also on the common path guidance algorithm is studied, and the analysis of the advantages and disadvantages of each algorithm and the range of use, due to the ant colony algorithm with intelligent search, can achieve the purpose of global optimization, robustness, self-organization, parallel performance is very prominent, and is suitable for nonlinear complex traffic system, so the ant colony algorithm of route guidance. Of course, any algorithm All have their own limitations and shortcomings, aiming at the shortcomings of ant colony algorithm on ant colony algorithm respectively to the state transition rule and pheromone updating rule is improved. So as to reduce travel users of invalid path search, and can choose the optimal path from the comprehensive factors. In this study, in order to meet the short term traffic flow prediction and route guidance algorithm practical requirements under the premise, give full play to the cloud computing platform in data storage and parallel processing advantage role, with the platform of Hadoop, the improved BP neural network algorithm and ant colony algorithm for the design and implementation of MapReduce, successfully designed a new short-term traffic flow forecasting and route guidance method, in the forecast, find good balance between accuracy and efficiency of induction, greatly enhanced the performance of the two algorithms in the practical aspects, and in reality In the experiment to verify the performance and practicability of the algorithm.

【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張治斌;李燕歌;;云計算下MapReduce多組容錯機(jī)制架構(gòu)的分析與研究[J];微電子學(xué)與計算機(jī);2014年01期

2 傅貴;韓國強(qiáng);逯峰;許子鑫;;基于支持向量機(jī)回歸的短時交通流預(yù)測模型[J];華南理工大學(xué)學(xué)報(自然科學(xué)版);2013年09期

3 陳曉晨;;對城市發(fā)展與城市交通問題的研究[J];科協(xié)論壇(下半月);2011年02期

4 王芳;戴永壽;王少水;;改進(jìn)的混沌遺傳算法[J];計算機(jī)工程與應(yīng)用;2010年06期

5 衛(wèi)小偉;巨永鋒;;智能交通系統(tǒng)及其在城市道路安全中的應(yīng)用[J];電子設(shè)計工程;2009年10期

6 張曉利;陸化普;;非參數(shù)回歸方法在短時交通流預(yù)測中的應(yīng)用[J];清華大學(xué)學(xué)報(自然科學(xué)版);2009年09期

7 匡胤;劉益和;黃迪明;;混沌理論在遺傳算法中的應(yīng)用研究[J];四川理工學(xué)院學(xué)報(自然科學(xué)版);2007年04期

8 許倫輝;劉正東;周文霞;習(xí)利安;;基于遺傳神經(jīng)網(wǎng)絡(luò)優(yōu)化模型的交通量預(yù)測[J];廣東工業(yè)大學(xué)學(xué)報;2006年04期

9 楊芳明;朱順應(yīng);;基于小波的短時交通流預(yù)測[J];重慶交通學(xué)院學(xué)報;2006年03期

10 李丹,張愛華,張志強(qiáng);最短路徑的并行加速比的研究[J];渤海大學(xué)學(xué)報(自然科學(xué)版);2004年03期

相關(guān)碩士學(xué)位論文 前2條

1 李張永;基于Hadoop的MapReduce計算模型優(yōu)化與應(yīng)用研究[D];武漢科技大學(xué);2015年

2 金逸文;城市快速路交通流數(shù)據(jù)修復(fù)方法研究[D];上海交通大學(xué);2008年

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