AUTOMATED PROCESS OF DETERMINING AREAS AND PLACES OF CONCENTRATION OF ROAD AND TRANSPORT ACCIDENTS IN THE URBAN TERRITORIAL COMMUNITY OF IVANO-FRANKIV

Abstract

This article examines the pressing issue of increasing road traffic accident (RTA) rates in Ukraine, particularly the sharp rise in accidents involving fatalities and injuries in 2024 compared to previous years. According to national statistics, over 37 % of traffic-related fatalities are vulnerable road users such as pedestrians and cyclists. The most frequent causes of accidents include speeding, improper maneuvers, and violations of intersection rules. In light of these challenges, the identification of accident concentration areas is vital for improving road safety and allocating resources effectively.The authors propose a modern, automated approach to detecting accident-prone areas based on geospatial clustering of RTAs. The method uses the Haversine formula to calculate geographic distances between individual accidents and groups them into clusters based on configurable parameters such as proximity, number of incidents, presence in an urban area, and time range.A web application and REST API were developed using ASP.NET Core and Google Maps JavaScript API to visualize clusters and allow users to filter data by year, road conditions, lighting, weather, involvement of vulnerable users, and presence of fatalities. This platform provides an interactive and data-driven means of identifying high-risk zones.The approach overcomes limitations of earlier methods that relied on Excel and outdated scripting languages like VBA, which restricted scalability and integration with modern web and mobile solutions. The implemented system is extensible, allowing for integration with municipal traffic monitoring systems or national road safety platforms. This facilitates more timely, data-informed decision-making and proactive safety measures, especially in areas with high concentrations of vulnerable road users.