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Short-Term Traffic Flow Forecasting Method Based on LSSVM Model Optimized by GA-PSO Hybrid Algorithm

Author

Listed:
  • Qichun Bing
  • Dayi Qu
  • Xiufeng Chen
  • Fuquan Pan
  • Jinli Wei

Abstract

Short-term traffic flow forecasting is one of the key issues in the field of dynamic traffic control and management. Because of the uncertainty and nonlinearity, short-term traffic flow forecasting remains a challenging task. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow forecasting method based on LSSVM model optimized by GA-PSO hybrid algorithm is put forward. Firstly, the LSSVM model is constructed with combined kernel function. Then the GA-PSO hybrid optimization algorithm is designed to optimize the kernel function parameters efficiently and effectively. Finally, case validation is carried out using inductive loop data collected from the north-south viaduct in Shanghai. The experimental results demonstrate that the proposed GA-PSO-LSSVM model is superior to comparative method.

Suggested Citation

  • Qichun Bing & Dayi Qu & Xiufeng Chen & Fuquan Pan & Jinli Wei, 2018. "Short-Term Traffic Flow Forecasting Method Based on LSSVM Model Optimized by GA-PSO Hybrid Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-10, November.
    • Handle: RePEc:hin:jnddns:3093596
    DOI: 10.1155/2018/3093596
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