Abstract: It turns out that the excepted effect gains verification, effectively improves the model accuracy, and makes more exact forecasting, which expects to be helpful to concerned departments and personnel to grasp the traffic market trend or make decisions. The grey system forecasting model, neural network forecasting model and support vector machine forecasting model are proposed in this paper. Taking the road goods traffic volume from the year of 1996 to 2003 in the whole country as a study case, the forecasting results are got by three methods. From the forecasting results, we can conclude that the accuracy of the support vector machine forecasting method is higher. Analyzing the characteristic of the combining forecasting method, based on the grey system forecasting model, neural network forecasting model and support vector machine forecasting model, the linear combining forecasting model and support vector machine combining forecasting model are set up. Compared with single prediction methods and linear combining forecasting method, the accuracy of the support vector machine combining forecasting method is higher.

Key words: grey system, neural network, support vector machine, combining forecasting, traffic volume

摘要： 对交通运量做出较为准确的预测，能对相关部门和人员把握运输市场或进行决策有所裨益。对灰色、神经网络和支持向量机的三个预测模型进行了研究，以全国1996 ～ 2003年公路货运量为例，对公路货运量进行了预测，经过比较，支持向量机的预测方法精度较高。在分析组合预测特性的基础上，提出了对灰色系统、神经网络和支持向量机三种预测方法结果进行了线性组合预测方法和支持向量机的组合预测方法。与单一预测方法结果和线性组合预测进行对比，支持向量机组合预测方法比较精确。

关键词: 灰色系统, 神经网络, 支持向量机, 组合预测, 交通量