【摘要】 针对复杂交通路段下的短时交通流量模型的参数估计问题,建立了基于宏观交通流量预测的状态空间模型,提出了基于正交自适应差分演化的无迹卡尔曼滤波(UKF)算法,解决交通流量预测动态模型的参数优化问题。对差分演化算法(DE)的初始化过程,使用基于正交设计和量化技术的交叉算子最大限度地提高种群的多样性,平衡差分演化算法的开采性和勘探性,更高效地搜索无迹卡尔曼滤波的模型参数。并针对UKF、DE的不同情况,分别采用不同的自适应策略提高调节算法性能。实验结果表明,相对于单独使用随机分布的方式初始化,或者根据经验设置模型参数的方法,使用正交设计方法的初始化策略、变异算子以及参数自适应控制策略的差分演化算法能够有效地节省计算资源,提升预测性能和精度,具有更高的鲁棒性。更多还原

【Abstract】 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.更多还原