Highway geometric design optimization for improved safety and its sensor visibility using decision trees

The application of decision trees in optimizing geometric design parameters for enhanced road safety and Intelligent Transportation System (ITS) sensor visibility is now an imperative. By utilizing a comprehensive dataset comprised of road geometry, ITS sensor locations, accident records, and traffic volume, decision tree models can be developed to predict optimal design parameters. In doing this, the results should show a high percentage of accuracy in predicting safe and visible designs. Furthermore, a decision tree-based feature selection process can identify road curvature, grade, sensor placement, and intersection type as critical factors influencing safety and sensor visibility. This study presented significant layouts and interactions between design parameters, while highlighting the importance of considering contextual relationships in geometric design using decision tree. Additionally, decision tree models were evaluated using accuracy, precision, and recall metrics, demonstrating robust performance. A sensitivity analysis was shown to assess model robustness. The study's findings suggest that integrating decision tree optimization into geometric design software can enhance evidence-based design practices. The study's outcome is relevant to policymakers, transportation engineers, and researchers seeking to improve road safety and ITS effectiveness.