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针对短时交通流数据的周期性、非线性和随机性的特点,提出一种基于复化Simpson公式的季节性灰色Fourier模型.在季节性GM(1, 1)模型的基础上,首先利用复化Simpson公式对背景值进行优化,然后用Fourier级数方法修正预测结果.将新模型应用于加拿大Whitemud Drive高速公路的交通流预测,数值计算结果表明:新模型的预测平均绝对值百分比误差为1.54%、拟合度为0.996 0,均优于传统的GM(1, 1)模型、季节性GM(1,1)模型和Fourier优化的季节性GM(1, 1)模型.
Abstract：According to the characteristics of the periodic fluctuation, nonlinearity and randomness of short-term traffic flow datum, a seasonal grey Fourier model based on compound Simpson formula is proposed. In the new model,the background value is first reconstructed by using the compound Simpson formula and the Fourier series method is then used to extract the periodic information to improve the residual error of the original seasonal GM(1, 1). Finally,the model is applied to traffic flow prediction of Canadian Whitemud Drive expressway. Numerical results show that the mean absolute percentage error and the fitting degree of the new model are 1.54% and 0.996 0, respectively. Both of them are better than those of the traditional GM(1, 1) model, the seasonal GM(1, 1) model and the seasonal GM(1, 1) model with Fourier optimization.
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基本信息:
中图分类号:U491.1
引用信息:
[1]沈琴琴,刘恒孜,王玥,等.基于季节性灰色Fourier模型的短时交通流预测[J].南通大学学报(自然科学版),2018,17(04):30-35.
基金信息:
国家自然科学基金项目(61771265);; 江苏省现代教育技术研究课题(2017-R-54054);; 国家级大学生创新训练计划项目(201710304038Z);; 江苏省高校自然科学基金面上项目(18KJB580012)
2018-12-20
2018-12-20