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The desire to reduce carbon dioxide emissions and the transition to electric transport are becoming key areas for the development of transport infrastructure in cities and agglomerations of the European part of Russia. For an effective transition to electric vehicles, it is necessary to develop a network of charging stations capable of meeting the growing demand for environmentally friendly modes of transport. The subject of the study is the analysis of the spatial and temporal characteristics of the movement of cars to optimize the location of the charging infrastructure. The aim of the work is to develop a methodological approach to determining the concentration zones of transport activity based on real data, which will make it possible to reasonably select locations for the installation of charging stations. This article discusses the approach of using the DBSCAN clustering algorithm to analyze the spatial and temporal characteristics of car movement based on real data on taxi rides in Omsk. This algorithm is implemented in the Python programming language. In the course of the study, the main spatial and temporal patterns of taxi movement in the studied region were identified and the initial and final points of taxi routes were combined into well-founded clusters. The analysis makes it possible to identify the main areas of concentration of transport activity, which forms the basis for further modeling of traffic flows in order to optimize the location of the charging infrastructure. The scope of the results includes the planning of urban transport infrastructure, the development of a network of charging stations for electric vehicles and the optimization of traffic flows. The findings of the study confirm the effectiveness of using the DBSCAN algorithm to identify areas of high traffic activity, which can significantly improve the quality of charging infrastructure planning and accelerate the transition to environmentally friendly transport.