Transport infrastructure has failed to keep pace with the rise in traffic demand over the years, resulting in congestion, especially in urban areas where traffic concentration is high. By observing and analysing the existing conditions of the transport network, it is possible to achieve certain improvements in traffic flow, especially on critical traffic routes, such as increasing road capacity, optimizing free passage time on traffic lights, preventing congestion and increasing safety. Therefore, there is a growing interest in software tools for processing, visualization and interpretation of traffic data. Using the visualization of traffic data, it is possible to detect key traffic parameters that describe the observed part of the road. Speed Transition Matrices were used in this thesis to visualize traffic data. They are a representation of the speed of a car during the transition between two consecutive road segments within certain time intervals. The input data used in this paper were generated using a SUMO simulator. The data is divided into two sets, one showing the normal traffic flow, while the other contains the congested traffic flow of a part of highway. Using the MongoDB database and the Python programming language for data pre-processing, speed transition matrices were computed. To determine the state of the traffic network, the point of the centre of mass was calculated for each matrix. In the analysis of the matrix, those that show traffic congestion were singled out. The software solution presented in this thesis enables the detection of congestion simulated in individual traffic scenarios and the determination of a sufficiently large time interval in which it is necessary to collect data for the quality of the assessment of the condition of the traffic network.