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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 12
3718-3724

Traffic flow forecasting using least squares support vector machine optimized by modified gravitational search algorithm

Xu Qinshuaia,b
He Qinga,b
Wei Kangyuana,b
a. College of Big Data & Information Engineering, b. Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2018.07.0383

Abstract

In order to improve the accuracy of traffic flow forecasting model based on least squares support vector machine(LSSVM), this paper proposed a novel modified gravitational search algorithm(TCK-AGSA) for parameters optimization. Firstly, this paper improved the Kbest function based on tent map, so that the algorithm had a mechanism to jump out of local optimum. Then, it introduced the guidance of global optimal to accelerate the movement of agents towards optimal solution. Furthermore, it introduced the evolutionary factor and converge factor into the weighted coefficient of agent's velocity to make the algorithm more adaptive. The simulation results for 12 benchmark functions show that the performance of TCK-AGSA is better than GSA and its variants. Finally, this paper proposed a LSSVM model optimized by TCK-AGSA, and selected the 2016 actual traffic flow data of Guizhou Expressway for experiment. The results show that the proposed model has better prediction accuracy, robustness and generalization capability.

Foundation Support

贵州省科技计划项目重大专项资助项目(黔科合重大专项字［2018］3002)
贵州省公共大数据重点实验室开放课题(2017BDKFJJ004)
贵州省教育厅青年科技人才成长项目(黔科合KY字［2016］124)
贵州大学培育项目(黔科合平台人才［2017］5788)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0383
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 12
Section: Algorithm Research & Explore
Pages: 3718-3724
Serial Number: 1001-3695(2019)12-042-3718-07

Publish History

[2019-12-05] Printed Article

Cite This Article

徐钦帅, 何庆, 魏康园. 改进引力搜索最小二乘支持向量机交通流预测 [J]. 计算机应用研究, 2019, 36 (12): 3718-3724. (Xu Qinshuai, He Qing, Wei Kangyuan. Traffic flow forecasting using least squares support vector machine optimized by modified gravitational search algorithm [J]. Application Research of Computers, 2019, 36 (12): 3718-3724. )

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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