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基于模糊信息粒化和CPSO-LS-SVM的城市轨道交通客流量组合预测
引用本文:汤旻安,张凯,刘星.基于模糊信息粒化和CPSO-LS-SVM的城市轨道交通客流量组合预测[J].测试科学与仪器,2018(1):32-41.
作者姓名:汤旻安  张凯  刘星
作者单位:兰州交通大学自动化与电气工程学院,甘肃兰州730070;兰州理工大学机电工程学院,甘肃兰州730050 兰州交通大学自动化与电气工程学院,甘肃兰州,730070
基金项目:National Natural Science Foundation of China(61663021),Science and Technology Support Project of Gansu Province(1304GKCA023),Scientific Research Project in University of Gansu Province(2017A-025)
摘    要:为了获得城市轨道交通客流量的变化趋势和更好地掌握客流量的波动范围,本文提出了一种基于模糊信息粒化和混沌粒子群算法(CPSO)优化最小二乘支持向量机(LS-SVM)的客流量波动范围组合预测模型.针对客流量的非线性和波动性,采用模糊信息粒化,将客流量数据根据需要按窗口提取有效信息,利用CPSO较强的全局搜索能力对LS-SVM预测模型的参数进行最优选取.最后运用组合模型预测2014年广州市地铁3号线体育西路站早高峰客流量波动范围,并与其他模型进行对比分析.仿真结果表明,本文组合预测模型能有效地跟踪客流量变化趋势,为预测未来一段时间内的短期客流量波动范围趋势提供了一种行之有效的方法.

关 键 词:城市轨道交通  客流量预测  最小二乘支持向量机  模糊信息粒化  混沌粒子群算法  urban  rail  transit  passenger  flow  forecast  least  squares  support  vector  machine  (LS-SVM)  fuzzy  information  granulation  chaos  particle  swarm  optimization(CPSO)

Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM
TANG Min-an,ZHANG Kai,LIU Xing.Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM[J].Journal of Measurement Science and Instrumentation,2018(1):32-41.
Authors:TANG Min-an  ZHANG Kai  LIU Xing
Abstract:In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine (LS-SVM) optimized by chaos particle swarm optimization (CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.
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