基于灰色马尔科夫链模型的交通量预测

摘要

Traffic volume is an unstable time series. Traffic volume forecast is a more complicated work under uncertain conditions or lacking of data. Gray Markov chain model is prediction model of the combination of Grey Theory and state transition behavior of Gray Markov Chain theory,which has the advantages of each method and overcomes the shortcomings of the Gray theory model. Based on the actual traffic data of Taiyuan YI-FEN Bridge section, a model of traffic volume prediction is established in this paper. The research shows that the forecast precision of the model in traffic volume is higher than traditional Gray GM(1,1) model.%交通量是一个不平稳的时间序列,在不确定性条件和缺乏数据资料的情况下,交通量的预测是一个较复杂的问题.灰色马尔科夫链模型是一种结合经典灰色理论和马尔科夫链的状态转移行为的预测模型.该模型在灰色预测理论的基础上,再对随机波动大的残差序列进行马尔科夫预测,实现了两者的优势互补,克服了两者的不足.以太原市漪汾桥断面的交通量的数据在传统灰色GM(1,1)预测模型的基础上建立交通量的灰色马尔科夫链模型,研究表明,该模型在交通量的预测方面相对传统的灰色GM(1,1)模型有更高的精度.