SVRGSA: A hybrid learning based model for short-term traffic flow forecasting

Lingru Cai, Qian Chen, Weihong Cai, Xuemiao Xu, Teng Zhou, Jing Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

41 Citations (Scopus)


Accurate and timely short-term traffic flow forecasting is a critical component for intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to complex non-linear data pattern of traffic flow. Support vector regression (SVR) has been widely employed in non-linear regression and time series prediction problems. However, the lack of knowledge of the choice of hyper-parameters in the SVR model leads to poor forecasting accuracy. In this study, the authors propose a hybrid traffic flow forecasting model combining gravitational search algorithm (GSA) and the SVR model. The GSA is employed to search optimal SVR parameters. Extensive experiments have been conducted to demonstrate the superior performance of the proposal.

Original languageEnglish
Pages (from-to)1348-1355
Number of pages8
JournalIET Intelligent Transport Systems
Issue number9
Publication statusPublished - 1 Sept 2019

ASJC Scopus subject areas

  • Transportation
  • Environmental Science(all)
  • Mechanical Engineering
  • Law


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