Analysis of Semi-Global Factors Influencing the Prediction of Accident Severity

With the growing diversity of road users and means of transport in Germany, it is becoming increasingly important to 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 are designed not only to predict accident severity but also to make the underlying patterns understandable for urban planners, safety personnel, and other interested parties through shapley-values as explainability methods. A particular challenge lies in presenting these complex relationships in a user-friendly way through visualizations and interactive maps.