A novel networked traffic parameter forecasting method based on Markov chain model
This paper introduces a novel networked traffic parameter forecasting method. Based on the detailed analysis of the literature, the paper describes the fundamental ideas. Then we select a typical traffic network in Beijing City. In order to simplify the problem, we classify the links using clustering analysis and find the representative links in each group. Furthermore, we introduce the Markov chain model to predict the traffic parameter. EM algorithm is applied to estimate the parameters of mixed Gaussian distributions, i.e., means, covariances and mixing coefficients. According to the regression equations between the representative links and the other links in the same group, we can obtain all the predicted traffic parameters of all the link in the road network. The case studies using real data from UTC-SCOOT system in Beijing have proved the effectiveness and applicability of the proposed method.