By analyzing and predicting the traffic states of urban road network, the formation of traffic congestion can be effectively alleviated, so as to improve the traffic capacity of urban road network. In this paper, firstly, we analyze and study the spatio-temporal correlation characteristics of traffic states based on the existing floating car data. At the same time, we extend the traffic conditions of urban road network from the upstream and downstream interaction to the global road network and complete the traffic congestion states discrimination of urban road network based on the spatio-temporal correlation. Secondly, according to the traffic jam aggregation and diffusion characteristics of local Moran's I, a mixed forest prediction method considering the spatio-temporal correlation characteristics of urban road traffic state is constructed by improving the existing random forest algorithm. Finally, an example is given to verify the effect of the prediction method on the short-term prediction of urban road network traffic states.

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.

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