The paper proposes an information-analytical methodology for ranking road segments of an urban street-road network based on the integration of geospatial data on traffic conditions and accident indicators. The relevance of the study is determined by the need to identify road segments characterized by a combination of increased traffic load, unfavorable traffic conditions, and elevated annual accident rates. Under current conditions of urban mobility development, the use of heterogeneous spatially referenced transport data requires analytical approaches that make it possible to combine different indicators within a unified procedure suitable for comparative assessment and decision support.
The object of the study is the street-road network of the city of Ternopil, while the subject is an information-analytical procedure for processing geospatial data on traffic load, travel speed, and accidents per kilometer in order to rank road segments by the level of problematicity and risk. The proposed approach uses traffic load and average speed indicators for morning and evening periods, as well as the annual accident rate represented as accidents/km. Since the initial indicators differ in scale and temporal aggregation, min-max normalization was applied to ensure data comparability and to provide a common basis for subsequent calculations.
The proposed methodology includes several successive stages: formation of a unified dataset for road segments, comparison of morning and evening traffic conditions, normalization of traffic indicators, calculation of a congestion index, derivation of aggregated segment characteristics, normalization of accident indicators, computation of an integral risk index, ranking of segments, and visual interpretation of the results. The congestion index was introduced as a generalized characteristic combining traffic load and speed, while the integral risk index combines congestion-related indicators with the annual accident rate. This made it possible to move from the analysis of individual traffic parameters to the integrated assessment of road segments from the standpoint of both traffic performance and safety.
Based on the calculated values, the road segments were ranked, the most problematic and priority segments for managerial intervention were identified, and the relationship between accident rate and traffic characteristics was assessed using Spearman’s rank correlation coefficient. The results revealed a weak positive relationship between accident rate and traffic load, a moderate positive relationship between accident rate and congestion indices, and a moderate negative relationship between accident rate and speed. These findings indicate that road segments with less favorable traffic conditions tend to demonstrate higher annual accident rates.
The scientific novelty of the study lies in the integration of traffic condition indicators and annual accident data within a single information technology for ranking road segments of an urban street-road network. The practical value of the study lies in the possibility of using the proposed approach to identify segments requiring priority attention in traffic management, to justify measures aimed at reducing accident risk, and to support the development of municipal transport monitoring and analytical systems.
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