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基于城市道路短时交通流数据的季节性特征和灰色建模的新信息优先原则,提出了一类新的分数阶季节性GM(1,1)预测模型。在GM(1,1)模型的基础上,首先,利用分数阶截断累加生成算子弱化了数据的季节波动性和随机性特征;然后采用粒子群优化算法寻求最佳阶数;最后,将新模型应用于江苏省南通市区的一主干道路进行模拟仿真。数值计算结果表明:新模型的平均绝对值百分比拟合误差为8.126 0%、预测误差为7.621 6%,均优于季节性滚动GM(1,1)模型、分数阶GM(1,1)模型和季节性离散GM(1,1)模型。
Abstract：Based on the seasonal characteristic of urban road traffic flow data and the principle of new information, a new fractional seasonal GM(1, 1) prediction model is proposed. In the new model, a fractional cycle truncation accumulated generation operator(FCTAGO) was firstly proposed to weaken the stochastic disturbances and the seasonal characteristics of the original sequence, and then the particle swarm optimization(PSO) algorithm was adopted to find the optimal fractional order. Finally, the new model was applied to stimulate a trunk road traffic flow of Nantong,Jiangsu Province. The numerical results show that the average absolute percentage of the new model has a fitting error of 8. 1260% and a prediction error of 7. 6216%, which are much better than those of the seasonal rolling GM(1, 1)model, fractional GM(1, 1) model and seasonal discrete GM(1, 1) model.
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基本信息:
DOI：10.12194/j.ntu.20191114001
中图分类号:U491.14
引用信息:
[1]沈琴琴,张智杰,齐绪存,等.基于分数阶季节性灰色模型的交通流预测[J],2021,20(02):37-42.DOI:10.12194/j.ntu.20191114001.
基金信息:
国家自然科学基金面上项目（91771265,11771225）;; 江苏省现代教育技术研究课题（2017-R-54054）;; 江苏省高校自然科学基金面上项目（18KJB580012）;; 南通市科技计划项目（JC2018142）;; 江苏省大学生创新训练计划项目（201810304129H,201810304079Y）