TANG Min-an1,2， ZHANG Kai1， LIU Xing1
(1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China；2. School of Mechanical and Electronical Engineering, Lanzhou University of Technology, Lanzhou 730050, China)
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.
Key words： urban rail transit; passenger flow forecast; least squares support vector machine (LS-SVM); fuzzy information granulation; chaos particle swarm optimization(CPSO)
CLD number： U293.13 Document code： A
Article ID：1674-8042（2018）01-0032-10 doi： 10.3969/j.issn.1674-8042.2018.01.005
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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
汤旻安1,2， 张 凯1， 刘 星1
（1. 兰州交通大学 自动化与电气工程学院， 甘肃 兰州 730070；2. 兰州理工大学 机电工程学院， 甘肃 兰州 730050）
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
引用格式： TANG Min-an， ZHANG Kai， LIU Xing. Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM. Journal of Measurement Science and Instrumentation, 2018, 9(1)： 32-41. ［doi： 10.3969／j.issn.1674-8042.2018.01.005］
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