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计算机系统应用英文版:2013,22(12):132-135
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基于指数平滑和马尔可夫链的短时交通流量预测
(西安理工大学 计算机科学与工程学院, 西安 710048)
Short-term Traffic Flow Forecasting Based on Exponential Smoothing and Markov Chains
(School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China)
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Received:May 17, 2013    Revised:June 17, 2013
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.
Abstract: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 future status forecast accuracy. An experiment on actual traffic flow has shown that, the proposed method improves forecast accuracy and has stronger adaptability.
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基金项目:国家自然科学基金(61172018);陕西省科技攻关项目(2011NXC01-12);陕西省教育厅科技项目(2010JC15)
引用文本:
李军怀,高瞻,王志晓,张璟.基于指数平滑和马尔可夫链的短时交通流量预测.计算机系统应用,2013,22(12):132-135
LI Jun-Huai,GAO Zhan,WANG Zhi-Xiao,ZHANG Jing.Short-term Traffic Flow Forecasting Based on Exponential Smoothing and Markov Chains.COMPUTER SYSTEMS APPLICATIONS,2013,22(12):132-135