基于灰色马尔科夫模型的机场安检危险品数量预测
发布时间：2018-11-21 13:20
【摘要】：机场安检的危险品数量具有动态、随机、非线性等特点,传统的GM(1,1)模型无法对其作出准确的预测。利用灰色GM(1,1)模型对2014年1—5月所查获的危险品数量进行计算、检验,并对6—8月的危险品数量进行预测。首先建立危险品数量的GM(1,1)模型,然后再对其预测值进行修正,结果表明,灰色马尔科夫模型的平均相对误差比灰色预测模型的平均相对误差减小了25.18%,表明灰色马尔科夫模型比单一的灰色预测模型的精度高,该模型是有效可行的,可为航空公司6—8月将要查获的危险品数量预测提供理论基础,以便引起相关部门的高度重视,并采取相应措施以保障旅客安全。
[Abstract]:The quantity of dangerous goods in airport security inspection is dynamic, stochastic and nonlinear, which can not be accurately predicted by the traditional GM (1 / 1) model. The gray GM (1 / 1) model was used to calculate and test the quantity of dangerous goods seized in January-May 2014, and to predict the quantity of dangerous goods in June-August. First, the GM (1K1) model for the quantity of dangerous goods is established, and then the prediction value is modified. The results show that the average relative error of the grey Markov model is 25.18% less than the average relative error of the grey prediction model. It shows that the grey Markov model is more accurate than the single grey prediction model, and the model is effective and feasible. It can provide a theoretical basis for the prediction of the quantity of dangerous goods to be seized by airlines in June-August. In order to attract the attention of relevant departments, and take appropriate measures to ensure passenger safety.
【作者单位】： 中国民航大学经济与管理学院;
【基金】：天津市高等学校人文社会科学研究项目(20102143) 中央高校基本科研业务费项目(2010D018) 中国民航大学校科研项目(2011kyE05)
【分类号】：V328
[Abstract]:The quantity of dangerous goods in airport security inspection is dynamic, stochastic and nonlinear, which can not be accurately predicted by the traditional GM (1 / 1) model. The gray GM (1 / 1) model was used to calculate and test the quantity of dangerous goods seized in January-May 2014, and to predict the quantity of dangerous goods in June-August. First, the GM (1K1) model for the quantity of dangerous goods is established, and then the prediction value is modified. The results show that the average relative error of the grey Markov model is 25.18% less than the average relative error of the grey prediction model. It shows that the grey Markov model is more accurate than the single grey prediction model, and the model is effective and feasible. It can provide a theoretical basis for the prediction of the quantity of dangerous goods to be seized by airlines in June-August. In order to attract the attention of relevant departments, and take appropriate measures to ensure passenger safety.
【作者单位】： 中国民航大学经济与管理学院;
【基金】：天津市高等学校人文社会科学研究项目(20102143) 中央高校基本科研业务费项目(2010D018) 中国民航大学校科研项目(2011kyE05)
【分类号】：V328
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