基于影响模型的短时交通流预测方法

根据复杂交通网络中多个节点之间交通流相互影响的特性，提出一种基于影响模型的短时交通流预测方法。分析交通网络中交通流预测的难点，引入随机过程中影响模型的理论对其进行建模。将每个节点的交通流处理为一个隐马尔科夫过程，整个网络由多个相互交互的隐马尔科夫过程组成，采用EM算法对模型参数进行训练。实验结果表明，该方法具有较高的预测精度，可较好地显示交通网络中多个节点之间交通流的交互规律以及动态演化规律。

Short-term Traffic Flow Forecasting Method Based on Influence Model

Aiming at the characteristic that traffic flow of different nodes influences each other in complex traffic network,this paper presents a novel short-term traffic flow forecasting method based on influence model.Based on the analysis of the difficulties of the traffic flow forecasting problem,this method introduces the theory of influence model to model.As the traffic flow of each node is considered as a hidden Markov process,the whole traffic network is composed of many interactive hidden Markov processes.This method uses the EM algorithm to learn parameters in the forecasting model.Experimental results show that the method not only has higher prediction accuracy,but also presents the interactive influence and dynamic evolution of the traffic flow of different nodes in traffic network.