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精确的短时交通预测是建立智能交通系统的一个重要前提,而具有明显周期性特点的交通流量的预测是其中的一个重要环节。为实现交通流量的准确预测,提出一种基于自适应惯性权重的粒子群优化(AωPSO)最小二乘支持向量机(LS-SVM)的短时交通流量预测方法,通过引入粒子种群多样性,设计自适应惯性权重调节方法,借助PSO算法的寻优能力实现LS-SVM参数的优化,减少人为因素对参数选择的影响,提高LS-SVM的泛化能力和预测精度。实验结果表明,与BP网络、LS-SVM等方法相比,该方法具有精度高、泛化能力强的特点。
Accurate short-term traffic forecasting is an important prerequisite for the establishment of an intelligent transportation system, and traffic flow forecasting with obvious cyclical characteristics is one of the important links. In order to achieve accurate prediction of traffic flow, a short-term traffic flow prediction method based on adaptive inertia weight of particle swarm optimization (AωPSO) least squares support vector machine (LS-SVM) is proposed. By introducing particle diversity, The adaptive inertia weight adjustment method is used to optimize the parameters of LS-SVM with the optimization ability of PSO algorithm, reduce the influence of human factors on parameter selection, and improve the generalization ability and prediction accuracy of LS-SVM. Experimental results show that compared with BP network and LS-SVM, this method has the characteristics of high precision and generalization ability.