中文摘要：

短时交通流预测是智能交通系统研究的一个重要问题．由于指数平滑法在对实测数据进行拟合时，预测精度不高，本文针对这一问题将指数平滑理论与马尔可夫链相结合，提出了指数平滑马尔可夫短时交通流量预测方法，借助于马尔可夫链来解决利用指数平滑法预测中存在的问题来缩小预测区间、提高预计算各状态加权中心及状态转移概率矩阵，以此来提高未来状态预测精度．采用实测交通流量进行仿真实验，结果表明，本文方法比常规指数平滑法具有更高的准确性，而且具有较强的适应性．

英文摘要：

Short-term traffic flow forecast is an important issue in Intelligent Transportation system. Due to the low forecast accuracy of exponential smoothing method in data fitting. In this paper, By combining exponential smoothing theory and Markov chain, we propose exponential-smoothing-Markov short-term traffic flow forecast method. With Markov chain, the method can solve the problems existing in exponential smoothing, i.e., by narrowing the forecast interval and improving status weighting centers and status transition probability matrices in pre-calculation, the proposed method can improve the fuatre status forecast accuracy. An experiment on actual traffic flow has shown that, the proposed method improves forecast accuracy and has stronger adaptability.