关键词: 短时交通流量, 相空间重构, 粒子群优化算法, 神经网络

Abstract: To improve the prediction precision of short-term traffic flow, this paper proposes a nonlinear short-term traffic flow prediction model based on parameters joint optimization algorithm, which uses the relationship between the phase space reconstruction and prediction model parameters. The phase space reconstruction and prediction model parameters are taken as particle of Improved Particle Swarm Optimization algorithm（IPSO） while the prediction accuracy of short-term traffic flow as the evaluation function of CPSO. The optimization parameters are obtained by collaboration among particles. The performance of  proposed model is tested by short-term traffic low data. The results show that the proposed method has improved the prediction precision compared with the traditional parameters optimization algorithm; it is a new way for the nonlinear prediction problem.

Key words: short-term traffic flow, phase space reconstruction, particle swarm optimization, neural network