Geometric Design Optimization for Improved Safety and ITS Sensor Visibility using Decision Trees

Abstract: The application of decision trees in optimizing geometric design parameters for an enhanced road safety and Intelligent Transportation System (ITS) sensor visibility is an imperative. By utilizing a comprehensive dataset (inclusive of literature) which comprised of road geometry, ITS sensor locations, accident records, and traffic volume, decision tree models can be developed to predict optimal design parameters. The results show a high percentage 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. The study also presents significant interactions between design parameters, while highlighting the importance of considering contextual relationships in geometric design. Additionally,  decision tree models were evaluated using metrics such as accuracy, precision, and recall, demonstrating robust performance. Sensitivity analysis conducted to assessing model robustness was featured. The findings of the study suggest that integrating decision tree optimization into geometric design software can enhance evidence-based design practices. The study's outcome is relevant to policy makers, transportation engineers, and researchers seeking to improve road safety and ITS effectiveness, providing actionable insights that can guide future infrastructure safety regulations, planning, and the development of smarter, more efficient transportation systems.