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Short-term traffic flow prediction algorithm based on orthogonal differential evolution unscented Kalman filter
YUAN Lei, LIANG Dingwen, CAI Zhihua, WU Zhao, GU Qiong
Journal of Computer Applications
2015, 35 (11):
3151-3156.
DOI: 10.11772/j.issn.1001-9081.2015.11.3151
A state-space model was established for the short-term traffic flow prediction problem under complex road conditions, which is based on macroscopic traffic flow forecasting. In order to solve the problem of parameter optimization on the dynamic traffic forecast model, a method to improve the performance of Unscented Kalman Filter (UKF) with orthogonal adaptive Differential Evolution (DE) was proposed. The orthogonal method maximized the diversity of the initial population in DE algorithm. The crossover operator in DE was optimized by the orthogonal method and the technology of quantification to balance the exploitation and exploration, which was more beneficial to find the model parameters of UKF. The experimental results show that, with respect to use random distribution to initialize the parameters, or set model parameters based on the experience, the use of orthogonal design method for initialization strategy, mutation operator and adaptive control strategy of parameters in differential evolution algorithm can effectively save computing resources, improve forecasting performance and accuracy, and provide better robustness.
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