Dynamic Model for Risk Assessment and Prevention of Road Traffic Accidents Involving Pedestrians

Pedestrian-involved road traffic accidents remain a major road safety concern and require a comprehensive analysis of the interactions within the “driver–vehicle–road–environment” system. Despite the substantial body of research in this field, most existing models rely on predefined parameters and experimental data, while the considerable information potential contained in the reasoning underlying judicial decisions concerning real-world road traffic accidents remains underutilized. This study proposes a novel approach in which the reasoning underlying judicial decisions is considered a structured source of technical information. Through a systematic analysis of the documented factual circumstances, technical expert reports, witness testimonies, and judicial findings, quantitative and qualitative characteristics of the transport system are extracted. This information is subsequently transformed into a system of linguistic variables describing driver behavior, vehicle technical characteristics, and road-environment conditions. The use of a symmetrically structured system of variables enables a consistent description of different vehicle–pedestrian interaction scenarios, regardless of the direction of motion and their relative positions. The geometric symmetry of the vehicle with respect to its longitudinal axis, together with the symmetrical representation of the traffic corridor, provides a unified coordinate framework for dynamic modeling and quantitative risk assessment. The proposed methodology was developed through an analysis of the reasoning underlying 105 judicial decisions concerning real-world pedestrian-involved road traffic accidents. As a result, 31 linguistic variables were identified and combined into an information vector describing each individual case. The analysis of the empirical distribution of the risk assessments shows mean values of 55.42% for driver-related factors, 49.29% for vehicle-related factors, and 35.77% for factors characterizing the road and environment. The results obtained indicate the dominant influence of the human factor on the formation of the integrated risk.