IR
Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM
Tang Minan1; Zhang Kai2; Liu Xing2
2018
Source PublicationJournal of Measurement Science and Instrumentation
ISSN1674-8042
Volume9Issue:1Pages:32-41
AbstractIn 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.
Keywordurban rail transit passenger flow forecast least squares support vector machine (LS-SVM) fuzzy information granulation chaos particle swarm optimization(CPSO)
Indexed ByCSCD
Language英语
WOS Research AreaTransportation
WOS SubjectTRANSPORTATION
CSCD IDCSCD:6208527
Document Type期刊论文
Identifierhttp://ir.lut.edu.cn/handle/2XXMBERH/73572
Collection兰州理工大学
Affiliation1.School of Automation and Electrical Engineering, Lanzhou Jiaotong University;;School of Mechanical and Electronical Engineering, Lanzhou University of Technology, ;;, Lanzhou;;Lanzhou, ;; 730070;;730050
2.School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
First Author AffilicationLanzhou University of Technology
First Signature AffilicationLanzhou University of Technology
Recommended Citation
GB/T 7714
Tang Minan,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,2018,9(1):32-41.
APA Tang Minan,Zhang Kai,&Liu Xing.(2018).Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM.Journal of Measurement Science and Instrumentation,9(1),32-41.
MLA Tang Minan,et al."Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM".Journal of Measurement Science and Instrumentation 9.1(2018):32-41.
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