Machine Learning-Based Information Approach for Analyzing Factors in Pedestrian Traffic Accidents

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.