Predictive Decision Tree Visualization for Traffic Accidents

The following presents a custom-developed program designed to visualize predictive decision trees for traffic accident analysis. This decision tree model is trained using historical accident data through machine learning algorithms, enabling the prediction of accident probabilities and potential outcomes. By analyzing key contributing factors and accident patterns, the decision tree provides valuable insights to help drivers and traffic management authorities develop enhanced safety strategies and make data-driven decisions. The program employs scikit-learn's DecisionTreeClassifier for model training, incorporating feature importance analysis to identify critical risk factors such as weather conditions, road type, and time of day. The visualization component utilizes matplotlib and graphviz libraries to generate clear, interpretable tree structures with decision nodes showing splitting criteria and leaf nodes displaying prediction probabilities. The system also supports real-time prediction capabilities by processing new input data through the trained model, generating immediate risk assessments along with corresponding safety recommendations. This lightweight yet powerful tool demonstrates significant potential in traffic safety applications through its combination of machine learning implementation and intuitive visual representation.