AI-supported analysis predicts accident severity in Mainz using machine learning models, highlighting contributing factors.
As road users and means of transport in Germany become more diverse, we must better understand the causes and influencing factors of serious accidents. The aim of this work is to develop an AI-supported analysis approach that identifies and clearly visualizes the causes of accidents and their impact on accident severity in the urban area of the city of Mainz. The machine learning models predict accident severity and use Shapley values as explainability methods to make the underlying patterns understandable for urban planners, safety personnel, and other stakeholders. A particular challenge lies in presenting these complex relationships in a user-friendly way through visualizations and interactive maps.
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Frank et al. (2025) studied this question.
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