%0 Journal Article %A LIU Jing %T Short-time traffic prediction model based on LSSVM optimized by artificial fish swarm algorithm %D 2013 %R %J Computer Engineering and Applications %P 226-229 %V 49 %N 17 %X In order to improve the prediction accuracy of short-term traffic flow, in view of parameters optimization problem for Least Squares Support Vector Machine(LSSVM), this paper proposes a short-term traffic prediction model based on Artificial Fish Swarm Algorithm(AFSA) and LSSVM(AFSA-LSSVM) which LSSVM parameters are optimized by improved AFSA, and the simulation experiment is carried out based on short-term traffic flow data. The simulation results show that the proposed model can obtain better LSSVM parameters and can more accurately reflect the changes of short-term traffic flow, and improves the short-term traffic prediction accuracy compared with the reference model, and it provides a new research method for the nonlinear short-term traffic flow prediction. %U http://cea.ceaj.org/EN/abstract/article_30913.shtml