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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 3
696-700

Short-term traffic flow forecasting based on kernel learning methods

Wang Qiuli
Li Jun
School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
DOI: 10.19734/j.issn.1001-3695.2017.09.0916

Abstract

Based on the powerful nonlinear mapping ability of kernel learning, this paper proposed a class of kernel learning method for the short-term traffic flow forecasting. Kernel recursive least squares(KRLS) method using approximate linear dependence(ALD) technique could reduce the computational complexity and storage capacity, the KRLS method was an online kernel learning method and was suitable for training on large-scale data sets. Kernel partial least square(KPLS) method utilized the covariance between input and output variables to extract latent features. Kernel extreme learning machine(KELM) method used the kernel function to substitute for the unknown nonlinear feature mapping of the hidden layer, in addition, the output weights of the networks could also be analytically determined by using regularization least square algorithm, hence KELM method provided better generalization performance at a much faster learning speed. In order to verify the validity of the proposed kernel learning method, this paper respectively applied the employed KELM, KPLS and ALD-KRLS methods to different traffic flow forecasting instances in different area, compared to the other methods under the same conditions. Experimental results show that the proposed kernel-based learning methods have higher forecasting accuracy and improves training speed in the short-term traffic flow forecasting.

Foundation Support

国家自然科学基金资助项目(51467008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.09.0916
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 3
Section: Algorithm Research & Explore
Pages: 696-700
Serial Number: 1001-3695(2019)03-011-0696-05

Publish History

[2019-03-05] Printed Article

Cite This Article

王秋莉, 李军. 基于核学习方法的短时交通流量预测 [J]. 计算机应用研究, 2019, 36 (3): 696-700. (Wang Qiuli, Li Jun. Short-term traffic flow forecasting based on kernel learning methods [J]. Application Research of Computers, 2019, 36 (3): 696-700. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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