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

Abstract EN

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.