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Road traffic accidents models using threshold levels of fuzzy linear regression

机译:道路交通意外模型使用阈值水平的模糊线性回归

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It has been hypothesized that number of road traffic accidents and road casualties are increased in line with the rapid recent increase in the variables of registered vehicles, population and road length. However the effects of these variables toward road traffic accidents are still inconclusive. Therefore, this paper develops models based on the variables which can be used to determine number of road traffic accidents in Malaysia. In order to explain the effects of these variables to road traffic accident, fuzzy linear regression models with three threshold levels, h=0.1, 0.5, 0.9 are examined. Historical data from the year 1974 to 2007 were collected to test performances of the models. The results show that by applying a multi-variable approach to fuzzy linear regression, the model provides not only crisp output but also output range for road traffic accident in Malaysia. The fuzzy linear regression model with threshold level h=0.1 was outperformed the other two models. The variables of registered vehicles and population were notable predictors to number of road traffic accidents in Malaysia.
机译:它已经假设道路交通事故和道路伤亡人数随着注册车辆,人口和道路长度的迅速增加而增加。然而,这些变量对道路交通事故的影响仍然不确定。因此,本文根据可用于确定马来西亚的道路交通事故数量的变量,开发模型。为了解释这些变量对道路交通事故的影响,检查具有三个阈值水平的模糊线性回归模型,H = 0.1,0.5,0.9。收集了1974年至2007年从1974年到2007年的历史数据来测试模型的表演。结果表明,通过应用多变量方法来模糊线性回归,该模型不仅提供了马来西亚公路交通事故的输出范围。具有阈值水平H = 0.1的模糊线性回归模型优于其他两个模型。登记车辆和人口的变量是马来西亚道路交通事故的数量的显着预测因子。

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