基于SOA-LSSVM的短时交通流量预测

Short-term Traffic Flow Prediction Based on SOA-LSSVM

针对短时交通流量存在的非线性与不确定性的问题,结合搜索者算法收敛精度高和最小二乘支持向量机计算速度快的优点,提出基于搜索者-最小二乘支持向量机(SOA-LSSVM)的流量预测模型,将该模型应用于短时交通流量预测,并与人工神经网络进行对比分析,结果表明,该模型具有较高的预测精度和泛化能力,适合于短时交通流量的预测,具有良好的推广应用价值。

In view of the nonlinear and uncertainty for short-term traffic flow characteristic, a forecasting model based on SOA-LSSVM is proposed, combining SOA’ s advantages of high convergence precision with LSSVM’ s superiority in fast solving speed. SOA is used in choosing regular parameter and nucleus parameter for LSSVM, thus, a optimal forecast model is established based on SOA-LSSVM. The model was used in short-term traffic flow prediction, and the application results shows that compared with traditional ANN, SOA-LSSVM model has a better prediction precision and application effect, and this model is fit for short-term traffic prediction with a good popularization value.