In most cases, the dynamic investigation of vehicle collisions with stationary obstacles concerns solutions to complex tasks related to the identification of occupant position in the vehicle. The motion of the bodies in the car is determined by the intensity of the inertial coordinate system, also known as moving reference frame, invariably fixed to the vehicle’s center of mass. The focus of the study is on how forces of inertia change their magnitude and direction in the car’s motion. This requires specific analysis carried out by dividing the vehicle trajectory into separate stages according to certain indicators, such as free motion, impact process, and post-impact residual motion. Particular attention has been paid to the impact itself, in which the forces of inertia are the most intense, and their magnitude and direction change abruptly. A solution to a Cauchy problem has been found, in which initial kinematic parameters of the crash process are considered, satisfying the kinematic values at rest position.

This This study presents a comparative analysis of two methodologies for predicting the risk of pedestrian-involved traffic accidents. The methodologies examined include one based on proportional risk distribution and another employing the machine learning algorithm Random Forest. The primary objective is to evaluate the applicability and accuracy of the proposed approaches through the processing and analysis of data derived from real-world cases of concluded legal proceedings. Linguistic variables, defined as risk factors, are classified and quantified based on expert evaluations. The results include interpolation models and graphical representations illustrating the risk severity according to the two methodologies. The analysis demonstrates that both methodologies are applicable for risk assessment, with the Random Forest algorithm providing superior accuracy and reliability in processing complex and heterogeneous data. Furthermore, a correlation analysis confirms a statistically significant linear relationship between the results of the two approaches. Visualization of the results through various graphical tools supports an objective comparison of the methodologies and their application in transport safety analysis.

This article presents a developed information-based approach for analyzing the factors contributing to pedestrian road traffic accidents (RTAs) through the application of advanced machine learning methods. The approach encompasses the identification and classification of key risk factors, the structuring of linguistic variables, and the quantitative assessment of their impact on risk. Machine learning algorithms are employed to model complex dependencies, alongside statistical and regression analyses to explore the causal relationships among the components of the transportation system-driver, vehicle, road, and environment. At the core of this approach lies the systematic utilization of empirical data and expert evaluations, enabling accurate risk predictions and assessments. The developed methodology facilitates integrated safety management and the creation of effective preventive strategies.