Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation

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.