Using Dynamic Bayesian Networks to Solve Road Traffic Congestion in the Sfax City. The development of a road traffic management system is used to model traffic movements in the city and to detect road axes that are often congested. In this case, it will be important to measure the demand for travel and to simulate its contribution to urban congestion in Sfax city. With a view to improving traffic management, our contribution focuses on the adaptation of a method capable of both identifying the different relevant variables of road traffic, modeling the probabilistic dependence structure on a road segment and to analyze the probabilities of urban congestion. The results produced by the diagnosis and analysis provided elements of response to the questioning of road traffic management. We found that the city of Sfax has shown a failure in intraurban transport services and the transport system in this region is not able to handle the expected increase in the volume of road traffic. We have demonstrated that the integration of public transport services contributes will improve traffic fluidity, and it is still able to make the public and urban space less polluting, more fluid and more attractive.

Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on SpatioTemporal 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 spatiotemporal 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 spatiotemporal correlation. Secondly, according to the traffic jam aggregation and diffusion characteristics of local Moranxe2x80x99s I, a mixed forest prediction method considering the spatiotemporal 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 shortterm prediction of urban road network traffic states.