To improve the accuracy of short-term traffic flow prediction,a prediction model based on improved sparrow search algorithm and optimized support vector regression was proposed.To solve the problems of slow convergence and easiness to fall into local optimum of SSA,the population initialization was combined with reverse learning and center walk mechanism to improve the population diversity.The segmented inertia weight and the butterfly optimization algorithm were introduced to improve the discoverer's position and expand the global search range and optimization ability in the early iteration of the algo-rithm.A follower location update mechanism based on Cauchy mutation was proposed to improve the local development ability and convergence speed of the algorithm in the later stage of iteration.An adaptive vigilant location update mechanism was designed to balance the search and development and improve the optimization accuracy of the algorithm.The improved sparrow search algorithm was applied to optimize SVR model,and a short-term traffic flow prediction model was constructed.Experi-mental results show that the improved model has better generalization ability and lower prediction error,and it can accurately predict the short-term traffic flow.
维普
万方数据