Traffic congestion analysis and traffic data extraction are key tasks for efficient traffic flow management in modern cities. This paper presents a system that combines automated data collection and processing from map services for subsequent analysis of road network graphs. The proposed method extracts information about traffic congestion using image processing of map services, which makes it independent of proprietary data sources. The approach is based on automated collection of map screenshots, parallel image processing and matching of the obtained information with road network graphs. At the processing stage, filtering by color ranges, selecting road segments and their mapping to the corresponding network segments are applied. Parallel execution of operations is used to increase efficiency, and validation and repeated query mechanisms are implemented to minimize errors. Experimental testing on the data of a large city showed that the method allows obtaining a detailed picture of traffic congestion, taking into account both temporal and spatial factors. The developed module can be used for traffic flow studies, traffic modeling and decision support in urban mobility. The main results are accurate identification of road segments with different degrees of congestion and their correct linking to the street network graph. The system has demonstrated its applicability for analyzing urban infrastructure and can be adapted to work with various map services, providing a wide range of opportunities for further research and practical use.
Povaliaev et al. (Thu,) studied this question.