灰色建模技术及其在道路交通事故管理中的应用研究
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摘要
灰色系统理论是系统科学中研究小样本、贫信息系统的理论与方法体系，灰色建模技术是灰色系统理论的核心，是连接灰色理论与实践应用的纽带。灰色建模技术已成功解决了经典方法难以解决的大量实际问题，具有广泛的应用背景和较强的应用价值。论文旨在对灰色建模技术及其在道路交通事故管理中的应用进行深入研究，以期在灰色数据变换技术、灰色关联分析技术、灰色预测技术和实际应用等领域获得一些新的研究成果，为灰色建模技术提供新思路、新方法，拓展灰色建模技术的应用范围，丰富和完善灰色建模体系，促进灰色系统理论与交通管理的融合发展。论文的主要研究内容和研究成果如下：
（1）通过对灰色数据变换技术提高序列建模的机理分析，明晰了灰色数据变换的构造机理。从灰色数据变换提高序列光滑性、减少序列级比偏差等角度，提出了灰色数据变换的构造准则，为灰色数据变换的构造提供了理论指导。构造了基于平均增长率的弱化变权缓冲算子，该算子不但满足缓冲算子的构造准则，还能通过变权系数调整其作用强度，丰富了缓冲算子体系。构造了基于线性函数与反余切函数的函数型数据变换，验证了所构造的函数型数据变换满足数据变换构造准则，拓展了函数型数据变换体系。
（2）针对周期波动序列的变动特征，给出了周期波动序列的周期确定方法和序列波动性的测度方法，将序列的周期性与波动性因素引入灰色关联分析模型，构建适用于周期波动序列的灰色关联分析模型，为分析具有周期波动特征的序列间的关联性提供了模型支撑。根据面板数据的时空特征建立了基于面板数据的灰色关联分析模型，为分析矩阵序列的关联性提供了新思路。提出了基于新信息优先原理的时序权重确定方法和基于差异驱动原理的指标权重确定方法，并在时序权重与指标权重确定的基础上构建了基于均值关联度的动态多指标评价模型，为动态多指标评价提供了新方法。
（3）针对传统的灰色GM(1,1)模型难以满足波动序列预测的需求，提出了利用振幅压缩变换和加权均值变换对波动序列进行处理，弱化波动序列的随机性，提高序列光滑性，使序列变化梯度趋于平缓，并利用变换后的数据建立GM(1,1)模型，为波动序列预测研究提供新思路。根据系统演化所呈现的阶段性特征：孕育阶段-匀速变化阶段-加速变化阶段-另一平衡状态，构建了能够反映系统动态演化规律的GM (1,1, t)模型，对该模型的建模机理、模型特性、参数估计等问题进行了探讨，拓展了灰色预测模型体系。针对交通事故系统特征变量的特点，构建参数可调的灰色GM(1,1)幂模型，利用灰色信息覆盖原理确定模型中参数的范围，并利用粒子群智能算法搜索最优参数，并对GM(1,1)幂模型的背景值进行智能优化。将灰色GM(1,N)模型与灰色关联分析相结合，通过灰色关联分析识别GM(1,N)模型中的相关因素，减少非关键因素被选入GM(1,N)模型的可能性。
（4）在灰色数据变换技术、灰色关联分析技术、灰色预测技术研究的基础上，利用灰色建模技术对我国道路交通事故的成因及演化趋势进行了分析，通过分析识别出影响我国道路交通安全的主要因素，并对我国道路交通事故的演化趋势进行了预测分析。根据道路交通事故成因分析与预测分析结果，提出了加强道路交通安全管理的对策。
Grey system theory is a system science to solve problems of “poor information and small amount ofsample data”. Grey modeling technique is the core of Grey system theory, and also the bond of GreyTheory and its application. Grey modeling technique has successfully solved a large number ofpractical problems that can’t be solved by traditional methods. And there is a wide applicationbackground and strong application value of grey modeling technique. This dissertation is aimed at greymodeling technique and its application on road traffic accidents management, hoping to work out somenew research results of grey data transformations technique, grey correlation analysis, grey predictiontechnique and practical application. It can provide new ideas, new methods for grey modelingtechnique, expand the range of application, enrich the grey modeling system, and also promote theintegration of grey system theory and traffic management. The main research contents of dissertationand research results are as follows:
(1) By analyzing the mechanism of the grey data transformation technique to the improvement ofsequence modeling, clear the structure mechanism of grey modeling technique. As the grey datatransformation technique can increase the smoothness and narrow the level deviation, propose theconstruction principles and theoretical guidance for the grey data transformation. Construct weakeningbuffer operator with variable weights based on the average growth rate. This operator can not onlysatisfied the construction principles of buffer operator, but also be able to adjust its intensity bychanging the weights, enriching the buffer operation system. Construct function-type datatransformation by linear function and anti-cotangent function, and verify its feasibility, expanding thefunction-type data transformation system.
(2)Considered the changing characteristic of periodic sequence, propose a method to determine theperiod and how to measure volatility of the periodicity sequence. Considered the periodicity andvolatility as factors of grey correlation analysis model, construct the grey correlation analysis modelwhich meets periodic sequence. It could be a model support to analyze the correlation between periodicsequences. According to the characteristics of panel data, grey correlation analysis model based onpanel data is established, it’s a new idea to analysis the correlation matrix sequence. Propose a methodto determine the timing weight based on new information priority principle, and a method to determinethe index weight based on discrepancy actuating principle. Then, construct a dynamic multi-indicatorsdecision making model based on the timing weight and index weight.
(3) Since the traditional GM (1,1) model is difficult to predict the volatility sequence, firstly deal with the volatility sequence by amplitude compression transformation and weighted mean valuetransformation, in ordor to weak the random fluctuation and improve the smoothness of sequence,and flatten the gradient of sequence. Through the transformed data to establish GM (1,1) model, newideas for the prediction of wave sequences provide new ideas. According to the system evolution stagecharacteristics, which are an embryonic phase, uniform change phase, accelerated phase andequilibrium, GM (1,1, t)model is constructed to reflect the dynamic evolution of system. Then makea research on the modeling mechanism, model characteristics, parameter estimation and other issues.Considered the characteristics of traffic behavior variable, parameters adjustable GM(1,1) power modelis constructed. Determine the range of parameter by grey information covering theory, work outthe optimal parameter by particle swarm intelligence algorithms search, and make intelligentoptimization of GM (1,1) power model. Combine GM (1,1) model with grey correlation analysis,reduce the possibility that non-key factors are elected to GM(1,N) model by grey correlation analysis.
(4) Based on the research on grey data transformations technique, grey correlation analyses, andgrey prediction technique, analyze the causes and evolution trends of road traffic accidents in China bygrey modeling technique, and get the main impact factors of road traffic safety. Then, make aprediction analysis on the evolution trends of road traffic accidents in China. According to the causesanalysis and the prediction analysis of road traffic accidents, targeted measures to enhance road trafficsafety are proposed.
（1）通过对灰色数据变换技术提高序列建模的机理分析，明晰了灰色数据变换的构造机理。从灰色数据变换提高序列光滑性、减少序列级比偏差等角度，提出了灰色数据变换的构造准则，为灰色数据变换的构造提供了理论指导。构造了基于平均增长率的弱化变权缓冲算子，该算子不但满足缓冲算子的构造准则，还能通过变权系数调整其作用强度，丰富了缓冲算子体系。构造了基于线性函数与反余切函数的函数型数据变换，验证了所构造的函数型数据变换满足数据变换构造准则，拓展了函数型数据变换体系。
（2）针对周期波动序列的变动特征，给出了周期波动序列的周期确定方法和序列波动性的测度方法，将序列的周期性与波动性因素引入灰色关联分析模型，构建适用于周期波动序列的灰色关联分析模型，为分析具有周期波动特征的序列间的关联性提供了模型支撑。根据面板数据的时空特征建立了基于面板数据的灰色关联分析模型，为分析矩阵序列的关联性提供了新思路。提出了基于新信息优先原理的时序权重确定方法和基于差异驱动原理的指标权重确定方法，并在时序权重与指标权重确定的基础上构建了基于均值关联度的动态多指标评价模型，为动态多指标评价提供了新方法。
（3）针对传统的灰色GM(1,1)模型难以满足波动序列预测的需求，提出了利用振幅压缩变换和加权均值变换对波动序列进行处理，弱化波动序列的随机性，提高序列光滑性，使序列变化梯度趋于平缓，并利用变换后的数据建立GM(1,1)模型，为波动序列预测研究提供新思路。根据系统演化所呈现的阶段性特征：孕育阶段-匀速变化阶段-加速变化阶段-另一平衡状态，构建了能够反映系统动态演化规律的GM (1,1, t)模型，对该模型的建模机理、模型特性、参数估计等问题进行了探讨，拓展了灰色预测模型体系。针对交通事故系统特征变量的特点，构建参数可调的灰色GM(1,1)幂模型，利用灰色信息覆盖原理确定模型中参数的范围，并利用粒子群智能算法搜索最优参数，并对GM(1,1)幂模型的背景值进行智能优化。将灰色GM(1,N)模型与灰色关联分析相结合，通过灰色关联分析识别GM(1,N)模型中的相关因素，减少非关键因素被选入GM(1,N)模型的可能性。
（4）在灰色数据变换技术、灰色关联分析技术、灰色预测技术研究的基础上，利用灰色建模技术对我国道路交通事故的成因及演化趋势进行了分析，通过分析识别出影响我国道路交通安全的主要因素，并对我国道路交通事故的演化趋势进行了预测分析。根据道路交通事故成因分析与预测分析结果，提出了加强道路交通安全管理的对策。
Grey system theory is a system science to solve problems of “poor information and small amount ofsample data”. Grey modeling technique is the core of Grey system theory, and also the bond of GreyTheory and its application. Grey modeling technique has successfully solved a large number ofpractical problems that can’t be solved by traditional methods. And there is a wide applicationbackground and strong application value of grey modeling technique. This dissertation is aimed at greymodeling technique and its application on road traffic accidents management, hoping to work out somenew research results of grey data transformations technique, grey correlation analysis, grey predictiontechnique and practical application. It can provide new ideas, new methods for grey modelingtechnique, expand the range of application, enrich the grey modeling system, and also promote theintegration of grey system theory and traffic management. The main research contents of dissertationand research results are as follows:
(1) By analyzing the mechanism of the grey data transformation technique to the improvement ofsequence modeling, clear the structure mechanism of grey modeling technique. As the grey datatransformation technique can increase the smoothness and narrow the level deviation, propose theconstruction principles and theoretical guidance for the grey data transformation. Construct weakeningbuffer operator with variable weights based on the average growth rate. This operator can not onlysatisfied the construction principles of buffer operator, but also be able to adjust its intensity bychanging the weights, enriching the buffer operation system. Construct function-type datatransformation by linear function and anti-cotangent function, and verify its feasibility, expanding thefunction-type data transformation system.
(2)Considered the changing characteristic of periodic sequence, propose a method to determine theperiod and how to measure volatility of the periodicity sequence. Considered the periodicity andvolatility as factors of grey correlation analysis model, construct the grey correlation analysis modelwhich meets periodic sequence. It could be a model support to analyze the correlation between periodicsequences. According to the characteristics of panel data, grey correlation analysis model based onpanel data is established, it’s a new idea to analysis the correlation matrix sequence. Propose a methodto determine the timing weight based on new information priority principle, and a method to determinethe index weight based on discrepancy actuating principle. Then, construct a dynamic multi-indicatorsdecision making model based on the timing weight and index weight.
(3) Since the traditional GM (1,1) model is difficult to predict the volatility sequence, firstly deal with the volatility sequence by amplitude compression transformation and weighted mean valuetransformation, in ordor to weak the random fluctuation and improve the smoothness of sequence,and flatten the gradient of sequence. Through the transformed data to establish GM (1,1) model, newideas for the prediction of wave sequences provide new ideas. According to the system evolution stagecharacteristics, which are an embryonic phase, uniform change phase, accelerated phase andequilibrium, GM (1,1, t)model is constructed to reflect the dynamic evolution of system. Then makea research on the modeling mechanism, model characteristics, parameter estimation and other issues.Considered the characteristics of traffic behavior variable, parameters adjustable GM(1,1) power modelis constructed. Determine the range of parameter by grey information covering theory, work outthe optimal parameter by particle swarm intelligence algorithms search, and make intelligentoptimization of GM (1,1) power model. Combine GM (1,1) model with grey correlation analysis,reduce the possibility that non-key factors are elected to GM(1,N) model by grey correlation analysis.
(4) Based on the research on grey data transformations technique, grey correlation analyses, andgrey prediction technique, analyze the causes and evolution trends of road traffic accidents in China bygrey modeling technique, and get the main impact factors of road traffic safety. Then, make aprediction analysis on the evolution trends of road traffic accidents in China. According to the causesanalysis and the prediction analysis of road traffic accidents, targeted measures to enhance road trafficsafety are proposed.
引文
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