中文摘要：

基于城市道路交通流按日周期变化的数据特征，提出一种可自动选择步长的灰色模型算法．将其应用到美国Minnesota的两个道路交通流的预测，并和传统灰色模型、历史平均法以及滑动平均法对比．数值实验结果表明：改进的灰色模型能够大幅降低预测绝对误差，预测精度高，稳定性好，适用于城市道路短时交通流的实时预测．

英文摘要：

A new algorithm of grey model is proposed on account of the periodic variation of traffic flow in urban road, which can automatically select the step size. In this paper, two traffic flows in the United States Minnesota are used to compare the performances of the different models： the modified grey model, the traditional grey model, the historical average method and the moving average method. The simulation results show that modified grey model can significantly reduce the absolute error, and has higher performances on forecasting. It is suitable for real-time forecasting of short time traffic flow in urban road.