基于违法数据分析的道路交通安全管理决策研究与应用
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摘要
本课题研究了交通违法行为的发生规律及预测算法、交通违法的成因、交通违法黑点鉴别方法,探讨了交通违法治理模糊决策方法,开发了交通违法事件分析与决策系统,为制定科学、合理的交通违法治理决策提供理论支持。
首先,研究了交通违法行为的成因,探讨了交通违法行为的共性。重点分析了交通违法行为的机理,从人、车、路和环境四个方面分析了交通违法产生的原因,以及交通违法导致交通事故的机理分析。该研究内容能够帮助深入理解交通违法行为,为下面的分析研究奠定了基础。
其次,研究了基于组合模型的交通违法信息预测分析算法,提出了基于小波理论和支持向量机方法的组合预测方法,并结合某省某地区的实际检测数据进行了效果验证。
然后,研究了交通违法黑点分析问题。借鉴交通事故黑点分析的方法,本文研究了交通违法黑点分析的方法。根据交通违法行为特征,提出了基于违法记分分值的当量违法总次数分析方法,并对违法黑点进行了预测。通过实例分析,将各种黑点分析方法对比验证,证明了黑点分析方法的有效性。
再次,研究了基于二级模糊推理的交通违法治理决策方法。探讨了交通违法治理的各种决策方法,包括决策树方法、模糊综合评判方法和多级模糊推理方法。各种方法均建立在现实工作实践经验的基础上,有较高的实用价值。
最后,开发了基于.net2005平台、oracle10g数据库的交通违法事件分析与决策系统,为进一步推广应用奠定了基础。结合实际,研究了容易引发交通事故的多发性交通违法行为的成因、危害及治理对策,在现实工作中具有很强的针对性和借鉴意义。
This subject mainly studied such problems as traffic violation occurrence regularities &prediction algorithms, causes of traffic violations and violation black-spots determining, suggested fuzzy decision theory methods of traffic violation management, developed a software system called Traffic Violation Analysis and Decision System, which was helpful in improving traffic safety management level.
Firstly, it studied the causes of traffic violation behavior, and discussed the common characteristics of traffic violations. It analyzed traffic violation mechamism in detail from four points including human being, vehicle, road and environment, and the tendency leading to accidents. This part was the base of the following ones.
Secondly, a kind of composite model of traffic violation prediction algorithm was proposed, which was the union of small-wave theory and SVM, and its feasibility was verified by the simulation example of some district in a province.
Thirdly, the analysis of traffic violations was studied. Reference to traffic accident black-spots analysis methods, the black-spots analysis methods of traffic violations were studied. According to the characteristics of traffic violations, an equivalent total violation analysis method based on penalty points was proposed. Prediction of black-spots of traffic violations was made too. Through experimental analysis, black-spots analysis methods can effectively analyze the locations of traffic violation black-spots.
Fourthly, traffic violation management decision algorithm based on second-order fuzzy inference theory was studied. This chapter analyzed several decision methods for traffic violation management, including decision tree method, fuzzy inference method and multi-order fuzzy inference method. Every method was based on practical experience, so it was more similar to the thought of human beings, and more robust.
At last, Traffic Violation Analysis and Decision System was developed, with.net 2005 development kit and oraclelOg database. This made our theory's application more accessible.Researches were made of the causes.hazards and counter-measures of some frequently-occurred traffic violateons,which will be much helpful to the practical work.
首先,研究了交通违法行为的成因,探讨了交通违法行为的共性。重点分析了交通违法行为的机理,从人、车、路和环境四个方面分析了交通违法产生的原因,以及交通违法导致交通事故的机理分析。该研究内容能够帮助深入理解交通违法行为,为下面的分析研究奠定了基础。
其次,研究了基于组合模型的交通违法信息预测分析算法,提出了基于小波理论和支持向量机方法的组合预测方法,并结合某省某地区的实际检测数据进行了效果验证。
然后,研究了交通违法黑点分析问题。借鉴交通事故黑点分析的方法,本文研究了交通违法黑点分析的方法。根据交通违法行为特征,提出了基于违法记分分值的当量违法总次数分析方法,并对违法黑点进行了预测。通过实例分析,将各种黑点分析方法对比验证,证明了黑点分析方法的有效性。
再次,研究了基于二级模糊推理的交通违法治理决策方法。探讨了交通违法治理的各种决策方法,包括决策树方法、模糊综合评判方法和多级模糊推理方法。各种方法均建立在现实工作实践经验的基础上,有较高的实用价值。
最后,开发了基于.net2005平台、oracle10g数据库的交通违法事件分析与决策系统,为进一步推广应用奠定了基础。结合实际,研究了容易引发交通事故的多发性交通违法行为的成因、危害及治理对策,在现实工作中具有很强的针对性和借鉴意义。
This subject mainly studied such problems as traffic violation occurrence regularities &prediction algorithms, causes of traffic violations and violation black-spots determining, suggested fuzzy decision theory methods of traffic violation management, developed a software system called Traffic Violation Analysis and Decision System, which was helpful in improving traffic safety management level.
Firstly, it studied the causes of traffic violation behavior, and discussed the common characteristics of traffic violations. It analyzed traffic violation mechamism in detail from four points including human being, vehicle, road and environment, and the tendency leading to accidents. This part was the base of the following ones.
Secondly, a kind of composite model of traffic violation prediction algorithm was proposed, which was the union of small-wave theory and SVM, and its feasibility was verified by the simulation example of some district in a province.
Thirdly, the analysis of traffic violations was studied. Reference to traffic accident black-spots analysis methods, the black-spots analysis methods of traffic violations were studied. According to the characteristics of traffic violations, an equivalent total violation analysis method based on penalty points was proposed. Prediction of black-spots of traffic violations was made too. Through experimental analysis, black-spots analysis methods can effectively analyze the locations of traffic violation black-spots.
Fourthly, traffic violation management decision algorithm based on second-order fuzzy inference theory was studied. This chapter analyzed several decision methods for traffic violation management, including decision tree method, fuzzy inference method and multi-order fuzzy inference method. Every method was based on practical experience, so it was more similar to the thought of human beings, and more robust.
At last, Traffic Violation Analysis and Decision System was developed, with.net 2005 development kit and oraclelOg database. This made our theory's application more accessible.Researches were made of the causes.hazards and counter-measures of some frequently-occurred traffic violateons,which will be much helpful to the practical work.
引文
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