Traffic accidents, especially severe ones, may never be completely avoidable, but their occurrence can be minimized. To support decision-makers in traffic safety, many institutions aim to identify the most dangerous road sections and assess risk levels. This master’s thesis focuses on developing and evaluating a predictive model to classify hazardous road sections and identify factors associated with traffic accident occurrence. The thesis outlines the creation of a comprehensive pipeline, covering stages from data acquisition and processing (specifically spatial data) to the implementation and evaluation of different models. A major focus was given to spatial data analyses, which provided valuable insights into the locations of traffic accidents and their surrounding conditions. In building predictive models, we found that creating negative samples of traffic accidents was essential for classification, which we generated in various ways. During testing, the neural network model performed best, achieving 90% accuracy and 81% recall. Our findings suggest that existing data can be used effectively with predictive models to classify dangerous road sections and determine the factors that most impact traffic accident occurrence. While the results are promising, additional experimental work is needed before practical application, particularly in refining training samples.
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