中文摘要：

为检验灰色模型及径向神经网络模型用于短时交通流预测的可行性及适用性，本文分析和比较了灰色模型GM（1，1）和RBF径向神经网络模型对短时交通流的预测效果。仿真实例表明，灰色模型不适合用于短时交通流预测，而径向神经网络能够准确预测短期交通流的未来变化趋势，当径向基函数的分布密度值在0．8～1．0之间时能够取得较高的预测精度。

英文摘要：

We analyzed and compared the effects of gray model GM （1 ,1） and RBF neural network model on short-term traffic flow prediction to test their feasibility and applicability. Practical instances show that gray model is inapplicable to the short-term prediction of traffic flow, but RBF neural network model is applicable. Moreover, we can acquire higher prediction accuracy when the distribution density of the radial basis function is from 0.8 to 1. 0.