Abstract
The article examines the assessment of traffic congestion within the street and road network of the city using geoinformation technologies. Traffic flow intensity is one of the key characteristics of road traffic, determining the occurrence of congestion both on major arterial streets and across urban districts as a whole. The collection and calculation of indicators that provide insight into the spatial differentiation of vehicle flow intensity and the distances between vehicles represent a labor-intensive stage in the study of fundamental traffic parameters. The most widespread method for collecting and monitoring traffic data is through stationary data acquisition. However, it should be noted that this approach is time-consuming. Given the rapid development of transport infrastructure, the speed at which stationary observations are conducted does not allow for timely updates on changes in street network congestion levels, and consequently, limits the ability to assess the real-time state of traffic flows. As an alternative to traditional data collection methods, modern sources of geospatial data can be utilized. Services originally designed for real-time traffic monitoring and route optimization can also serve as valuable data sources for traffic congestion assessment models and for studying vehicle emission levels. This study proposes a simulation-based model for evaluating congestion within the urban street network, which enables either the rapid elimination of congestion or its prevention, taking into account the distance between vehicles. The proposed methodology has been tested at the district level and on the primary arterial roads of the city of Ust-Kamenogorsk.