基于Spark的改進SA-SVR短時交通預測研究
發(fā)布時間：2018-10-31 15:58
【摘要】：科技的快速發(fā)展為人們的生活帶來了便捷,但同時也帶來了一些負面影響。交通事故、道路擁堵、以及車輛尾氣排放帶來的全球變暖等,這些交通問題作為經(jīng)濟發(fā)展的負面附屬品,是眾多問題中急需解決的一個。自交通問題出現(xiàn)之時,對于交通問題的解決方案的研究從未停步,隨著智能時代的到來,智能交通系統(tǒng)的概念被提了出來。智能交通系統(tǒng)作為目前解決交通問題的首選,而短時交通流作為智能交通系統(tǒng)的一部分更是得到了研究人員的重視。但是交通流并非是一成不變的,它是一個非平穩(wěn)的易受外界環(huán)境干擾的非線性系統(tǒng),并且擁有海量的交通流數(shù)據(jù),隨著時間的推移這些數(shù)據(jù)也在不斷的增長。如何處理這些海量的數(shù)據(jù)并達到交通流預測的精確性和實時性要求成為近年來的主要研究方向。本文從研究提高短時交通流預測的準確性和實時性出發(fā),主要研究內(nèi)容包括:(1)研究了適用于處理小樣本非線性的支持向量回歸機(SVR)。在已有的基礎(chǔ)上,對交通流和交通流的數(shù)據(jù)特點進行研究,基于交通流和交通流數(shù)據(jù)的特點研究了比較實用的短時交通流預測模型,經(jīng)過研究對比和實驗,驗證SVR作為短時交通流預測的可行性和實用性。(2)改進了適用于處理大型組合優(yōu)化的模擬退火算法(SA),將其應用于支持向量回歸機進行參數(shù)優(yōu)化。在選擇支持向量回歸機的基礎(chǔ)上,對支持向量回歸機的研究發(fā)現(xiàn)支持向量機的參數(shù)對于整個模型的預測結(jié)果有著至關(guān)重要的作用,為了達到建立基于最優(yōu)參數(shù)的短時交通流預測模型,本文研究對比其他傳統(tǒng)參數(shù)優(yōu)化算法,確立并改進了適用于處理大型組合優(yōu)化的模擬退火算法,基于改進后的模擬退火算法對支持向量回歸機進行參數(shù)優(yōu)化,并基于最優(yōu)參數(shù)建立了預測模型,解決了短時交通流預測中的預測準確性問題。(3)建立了Spark平臺下的SA-SVR預測模型。隨著交通流數(shù)據(jù)量的增加,在處理海量的交通流數(shù)據(jù)的過程中,單機模式下的預測模型由于物理因素的限制無法滿足短時交通流預測對于預測實時性的要求,為了解決預測時間的問題,本文在大數(shù)據(jù)時代的背景下研究對比選擇具有分布式并行處理能力的Spark技術(shù)對支持向量回歸機做大規(guī)模的并行算法訓練,并結(jié)合了支持向量回歸機處理非線性小樣本事件的特性和Spark的并行處理時間短的優(yōu)點,建立了Spark平臺下的SA-SVR預測模型。實驗證明,此模型在保證了預測精度的前提下縮短了預測的時間,同時滿足了短時交通流預測對于精確性和實時性的要求。本文基于預測模型進行了三組對比實驗,分別是RBF神經(jīng)網(wǎng)絡與支持向量回歸機模型、網(wǎng)格算法與模擬退火算法及改進后的模擬退火算法參數(shù)優(yōu)化模型、單機模式下與Spark并行模式下的預測模型實驗對比。這三組對比實驗結(jié)果證明了基于改進的模擬退火算法對支持向量回歸機進行參數(shù)優(yōu)化后的模型在Spark環(huán)境下比傳統(tǒng)的算法及單機模式下的預測更具有競爭力,Spark平臺下的該模型在預測過程中不僅解決了短時交通流預測的精確性問題,也解決了短時交通流預測中的預測效率問題,總體提高短時交通流預測中處理交通流數(shù)據(jù)的能力及預測的精確性和實時性。本文的主要創(chuàng)新點是將支持向量回歸機的稀疏性特點與分布式集群Spark系統(tǒng)的并行處理能力相結(jié)合,在Spark平臺下進行大規(guī)模SVR訓練,由此建立了Spark平臺下的SA-SVR短時交通流預測模型,該模型很好地解決了短時交通流預測的精確性和實時性問題。
[Abstract]:The rapid development of science and technology brings convenience to people's life, but at the same time has some negative effects. These traffic problems, such as traffic accidents, road congestion and global warming caused by vehicle exhaust emissions, are one of the many problems in the economy. With the advent of the traffic problem, the research on the traffic problem has never stopped. With the advent of the smart age, the concept of the intelligent transportation system has been raised. Intelligent transportation system is the first choice to solve traffic problem, while short-time traffic flow is regarded as part of intelligent transportation system. But the traffic flow is not immutable, it is a non-linear system that is non-stationary and easily disturbed by external environment, and has massive traffic flow data, and these data are constantly increasing over time. How to deal with these massive amounts of data and achieve the accuracy and real-time requirements of traffic flow prediction has become the main research direction in recent years. In order to improve the accuracy and real-time performance of short-time traffic flow prediction, the main research contents include: (1) the support vector regression machine (SVR) suitable for processing small sample non-linearity is studied. Based on the existing data characteristics of traffic flow and traffic flow, a practical short-time traffic flow prediction model was studied based on the characteristics of traffic flow and traffic flow data. (2) The simulated annealing algorithm (SA), which is suitable for processing large-scale combination optimization, is applied to support vector regression machine for parameter optimization. On the basis of selecting the support vector regression machine, the research of support vector regression machine has found that the parameter of support vector machine plays an important role in the prediction result of the whole model, in order to reach the short-time traffic flow prediction model based on the optimal parameters, Compared with other traditional parameter optimization algorithms, this paper establishes and improves the simulated annealing algorithm suitable for processing large-scale combination optimization, optimizes the parameters based on the improved simulated annealing algorithm, and establishes a prediction model based on the optimal parameters. and solves the problem of prediction accuracy in short-time traffic flow prediction. (3) The SA-SVR prediction model under the Spark platform is established. with the increase of the amount of traffic flow, in the process of processing mass traffic flow data, the prediction model in the stand-alone mode cannot satisfy the requirement of short-term traffic flow prediction to predict the real-time performance due to the limitation of physical factors, and in order to solve the problem of the prediction time, In this paper, in the background of the large data era, we study the Spark technology with distributed parallel processing ability to train the support vector regression machine in a large scale, The SA-SVR prediction model under the Spark platform is established by combining the advantages of the support vector regression machine to deal with the nonlinear small sample events and the short parallel processing time of Spark. Experimental results show that this model can shorten the forecast time on the premise of ensuring the prediction accuracy, and meet the requirement of short-time traffic flow prediction on the accuracy and real-time performance. In this paper, three groups of contrast experiments are carried out based on the prediction model, which are the RBF neural network and the support vector regression model, the mesh algorithm and the simulated annealing algorithm and the improved simulated annealing algorithm parameter optimization model, which is compared with the prediction model in the Spark parallel mode under the stand-alone mode. Compared with the traditional algorithm and the single-stand model, the model based on the improved simulated annealing algorithm is more competitive in the Spark environment than in the traditional algorithm and the stand-alone mode. The model not only solves the accuracy problem of short-time traffic flow prediction in the prediction process, but also solves the problem of prediction efficiency in short-time traffic flow prediction, and improves the capability of processing traffic flow data and the prediction accuracy and real-time performance in the short time traffic flow prediction. The main innovation point in this paper is to combine the sparse features of support vector regression machine with the parallel processing capability of distributed cluster Spark system, carry out large-scale SVR training under the Spark platform, and set up the SA-SVR short-time traffic flow prediction model under the Spark platform. The model well solved the accuracy and real-time problem of short-time traffic flow prediction.
【學位授予單位】：東華理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.14
[Abstract]:The rapid development of science and technology brings convenience to people's life, but at the same time has some negative effects. These traffic problems, such as traffic accidents, road congestion and global warming caused by vehicle exhaust emissions, are one of the many problems in the economy. With the advent of the traffic problem, the research on the traffic problem has never stopped. With the advent of the smart age, the concept of the intelligent transportation system has been raised. Intelligent transportation system is the first choice to solve traffic problem, while short-time traffic flow is regarded as part of intelligent transportation system. But the traffic flow is not immutable, it is a non-linear system that is non-stationary and easily disturbed by external environment, and has massive traffic flow data, and these data are constantly increasing over time. How to deal with these massive amounts of data and achieve the accuracy and real-time requirements of traffic flow prediction has become the main research direction in recent years. In order to improve the accuracy and real-time performance of short-time traffic flow prediction, the main research contents include: (1) the support vector regression machine (SVR) suitable for processing small sample non-linearity is studied. Based on the existing data characteristics of traffic flow and traffic flow, a practical short-time traffic flow prediction model was studied based on the characteristics of traffic flow and traffic flow data. (2) The simulated annealing algorithm (SA), which is suitable for processing large-scale combination optimization, is applied to support vector regression machine for parameter optimization. On the basis of selecting the support vector regression machine, the research of support vector regression machine has found that the parameter of support vector machine plays an important role in the prediction result of the whole model, in order to reach the short-time traffic flow prediction model based on the optimal parameters, Compared with other traditional parameter optimization algorithms, this paper establishes and improves the simulated annealing algorithm suitable for processing large-scale combination optimization, optimizes the parameters based on the improved simulated annealing algorithm, and establishes a prediction model based on the optimal parameters. and solves the problem of prediction accuracy in short-time traffic flow prediction. (3) The SA-SVR prediction model under the Spark platform is established. with the increase of the amount of traffic flow, in the process of processing mass traffic flow data, the prediction model in the stand-alone mode cannot satisfy the requirement of short-term traffic flow prediction to predict the real-time performance due to the limitation of physical factors, and in order to solve the problem of the prediction time, In this paper, in the background of the large data era, we study the Spark technology with distributed parallel processing ability to train the support vector regression machine in a large scale, The SA-SVR prediction model under the Spark platform is established by combining the advantages of the support vector regression machine to deal with the nonlinear small sample events and the short parallel processing time of Spark. Experimental results show that this model can shorten the forecast time on the premise of ensuring the prediction accuracy, and meet the requirement of short-time traffic flow prediction on the accuracy and real-time performance. In this paper, three groups of contrast experiments are carried out based on the prediction model, which are the RBF neural network and the support vector regression model, the mesh algorithm and the simulated annealing algorithm and the improved simulated annealing algorithm parameter optimization model, which is compared with the prediction model in the Spark parallel mode under the stand-alone mode. Compared with the traditional algorithm and the single-stand model, the model based on the improved simulated annealing algorithm is more competitive in the Spark environment than in the traditional algorithm and the stand-alone mode. The model not only solves the accuracy problem of short-time traffic flow prediction in the prediction process, but also solves the problem of prediction efficiency in short-time traffic flow prediction, and improves the capability of processing traffic flow data and the prediction accuracy and real-time performance in the short time traffic flow prediction. The main innovation point in this paper is to combine the sparse features of support vector regression machine with the parallel processing capability of distributed cluster Spark system, carry out large-scale SVR training under the Spark platform, and set up the SA-SVR short-time traffic flow prediction model under the Spark platform. The model well solved the accuracy and real-time problem of short-time traffic flow prediction.
【學位授予單位】：東華理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.14
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